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Employing AI-driven predictive analytics to enhance patient outcomes and streamline healthcare processes
uncertain
Show all credibility signals
- not scoredDOI registered: Not checked or no result supplied; no credibility inference made.
- not scoredDOI resolves: Not checked or no result supplied; no credibility inference made.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch expression of concern: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch correction: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch reinstatement: Not checked or no result supplied; no credibility inference made.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Predicting clinical outcomes from patient care pathways represented with temporal knowledge graphs
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Model-Driven Performance Prediction of Systems of Systems
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Data-Driven Prediction of Speech-in-Noise Performance: A Systematic Review and Meta-analysis of Machine Learning Models.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Construction of a diagnostic model for nasopharyngeal carcinoma using a consensus machine learning approach and study of immune infiltration characteristics
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Enhancing adult lifelong learning through data fusion algorithms and quality assurance
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Predicting food taste with bound-driven optimization.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Prediction of human intestinal passive membrane permeability using physiologically informed machine learning models.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
RadarDiT: An advanced radar echo extrapolation model for three gorges reservoir area via diffusion transformer
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Aquifer-differentiated process-guided GRU for multi-timescale groundwater-level prediction.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
HumanToxPredict: An interpretable data-driven framework for human oral TDLo prediction and toxicity assessment of pharmaceuticals and bioactive compounds.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
A prognostic model for use at labour onset to estimate the risk of severe fetal compromise at birth: a retrospective, model development and validation study.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
A Model-Based Joint Feature Fusion framework for limited-view photoacoustic tomography reconstruction.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Large language model-assisted curation and sequence-informed modelling reveal membrane and genetic determinants of antibiotic resistance gene removal.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Diffusion Model-Driven Spectral Noise Modeling and Generative Reconstruction Strategy.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Drug-Likeness, Druggability, and Molecular Developability via Chemometric Evaluation: A Review.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Learning to Estimate RIS-Aided mmWave Channels
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Preoperative 3D morphological phenotyping of hepatocellular carcinoma via a foundation model: a non-invasive biomarker for recurrence risk stratification.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Model-Driven Performance Prediction of Systems of Systems
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Leveraging Artificial Intelligence to Predict Posterior Malleolus Fracture Extension in Tibial Shaft Fractures
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
RAGP: a retrieval-augmented deep learning model for genomic prediction in crop breeding.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
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A pre-trained foundation model framework for multiplanar MRI classification of extramural vascular invasion and mesorectal fascia invasion in rectal cancer
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Scoping review of methodology for aiding generalisability and transportability of clinical prediction models.
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Data-driven occupancy model to support the calibration of simulations for overheating in residential buildings
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Spatially guided translation from histology images to transcriptomic profiles using foundation model-driven contrastive learning.
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A hybrid car-following behavior prediction framework for snowy weather based on stacking ensemble learning.
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AI-Driven Integration of Deep Learning With Lung Imaging, Functional Analysis, and Blood Gas Metrics for Perioperative Hypoxemia Prediction
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Collective mind: Towards practical and collaborative auto-tuning
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Brain-inspired predictive visual attention for spatiotemporal semantic understanding of complex film and television videos.
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Artificial intelligence-driven imaging-based prediction models for hemorrhagic transformation after mechanical thrombectomy in acute ischemic stroke: a systematic review and meta-analysis.
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Integrating BIM, Machine Learning, and PMBOK for Green Project Management in Saudi Arabia: A Framework for Energy Efficiency and Environmental Impact Reduction
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Composition Design and Property Prediction for AlCoCrCuFeNi High-Entropy Alloy Based on Machine Learning
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A Hierarchical Three-Stage Feature Selection Strategy for Efficient and Generalizable sEMG-Based Joint Torque Estimation in Athletic Training and Rehabilitation.
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[Predicting the risk of proximal junctional kyphosis after modified grade Ⅳ osteotomy for thoracolumbar old fractures with kyphotic deformity using machine learning].
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RESEARCH COLLECTION / FLAGSHIPS 61–70 OF 90 Yin-Yang Dynamics & Five-Phase Intelligent Acupuncture at the Limit STARLI Acupuncture Trilogy | F61–F70 of 90 | Author 33 | Research Design Working Edition v1.0 This ten-volume research-design collection develops testable questions inspired by yin-yang dynamics and five-phase philosophy, spanning bounded feedback models, autonomic coupling, physiological transitions, predictive digital twins, circadian timing, multisystem networks, neuroendocrine-immune associations, explainable AI, recovery resilience and simulated multiobjective optimization. Traditional terms serve as conceptual labels, not evidence of unmeasured energies or clinical benefits. Two five-theme research units assemble ten English 777-page working editions: 300 conditional mathematical model plates, 820 chapter openings, 4,100 separately paginated investigations, 2,000 Research Gates and 330 Phoenix Answer Embryos. Designs specify measurable targets, orthogonal checks, concealed tests, rival explanations and stop decisions. These are proposed research protocols, not outcomes of clinical trials or validated therapies. Research Unit I · F61–F65 / Yin-Yang Dynamics F61 YIN-YANG DYNAMIC BALANCE AND INTELLIGENT ACUPUNCTURE CONTROL AT THE LIMIT — Models multichannel return to a prespecified physiological reference band in a hardware-in-the-loop simulator. Blinded reference reversals and intervention-order swaps challenge apparent gains against passive relaxation, PID control and linear model predictive control. No treatment decisions are made. F62 YIN-YANG INTERDEPENDENCE AND BIDIRECTIONAL AUTONOMIC REGULATION AT THE LIMIT — Tests lagged covariance between independently supported autonomic proxy channels. Masked breathing and posture swaps, pressure witnesses and matched univariate forecasts probe common-input artifacts; the model must refuse claims when sympathetic and parasympathetic effects cannot be identified separately. F63 YIN-YANG TRANSFORMATION AND HUMAN PHYSIOLOGICAL STATE TRANSITIONS AT THE LIMIT — Predeclares stress–rest–recovery state labels and scores held-out transition predictions from permitted non-invasive archives. Concealed task/rest order and frozen hidden Markov and time-only comparators test whether transitions survive label leakage and ordinary recovery trends. F64 YIN-YANG IMBALANCE CONCEPTS AND PERSONALIZED ACUPUNCTURE DIGITAL TWINS AT THE LIMIT — Asks whether blinded traditional-pattern labels add out-of-site AUC or log-likelihood beyond age, symptoms and physiology. Rater exchanges, locked pipelines and random-label controls interrogate observer leakage; retrospective twin forecasts do not authorize autonomous acupuncture. F65 YIN-YANG TEMPORAL PHILOSOPHY AND CIRCADIAN ACUPUNCTURE CONTROL AT THE LIMIT — Tests circadian-phase forecasts against matched cosinor and sleep–light history models. Blinded shifts of archived light exposure and sleep timing probe whether apparent traditional timing information persists after independent phase calibration; the work remains non-interventional. SECOND RESEARCH UNIT / FIVE-PHASE NETWORKS & SCIENTIFIC EVIDENCE Research Unit II · F66–F70 / Five-Phase Models F66 FIVE-PHASE GENERATION-RESTRAINT PHILOSOPHY AND HUMAN MULTISYSTEM NETWORK MODELING AT THE LIMIT — Compares a locked five-node physiological graph with sparse and autoregressive network baselines. Concealed sensor-to-phase permutations probe decorative labeling; held-out log scores and edge stability must improve before assigning predictive value to the proposed topology. F67 FIVE-PHASE PHILOSOPHY AND NEUROENDOCRINE-IMMUNE NETWORKS AT THE LIMIT — Examines delayed hormone–immune associations in permitted archived assays. Resampling frequency challenges and diurnal-adjusted regressions test whether apparent network delays are caused by circadian trends or shared sampling clocks; impossible biological lags trigger rejection. F68 FIVE-PHASE PATTERN DIFFERENTIATION AND EXPLAINABLE AI IN MEDICINE AT THE LIMIT — Tests whether blinded pattern categories add held-out discrimination and calibration beyond conventional clinical covariates. Hidden institution/annotator shifts, permuted labels and external validation confront leakage, annotation bias and unreliable diagnostic confidence; no automated diagnosis is claimed. F69 FIVE-PHASE CYCLIC PHILOSOPHY AND PHYSIOLOGICAL NETWORK RESILIENCE AT THE LIMIT — Measures normalized residual recovery after ethically bounded observations or simulated network disruptions. Concealed removal of a simulated node and passive-return or linear-dynamics controls test whether cyclical mapping adds anything beyond conventional feedback; protective thresholds remain absolute. F70 FIVE-PHASE PHILOSOPHY AND MULTIOBJECTIVE INTELLIGENT ACUPUNCTURE OPTIMIZATION AT THE LIMIT — Examines stability of a simulated Pareto front for benefit, burden and uncertainty within a hard safety set. Sealed comfort/uncertainty priority swaps expose arbitrary weight selection; the design does not perform or recommend human acupuncture interventions. Physical Scope, Falsifiability & Evidence Status The studies are restricted to consented or authorized de-identified non-invasive recordings, archived assays, synthetic data, offline analyses and simulation or hardware-in-the-loop testing. There are no delivered acupuncture treatments, clinical trials or established efficacy findings. Conventional matched-budget baselines, fixed labels, independent physical witnesses and explicit stopping criteria are required before any candidate advantage can be claimed. The stated methodological ancestry is the STARLI 90 Flagship Master v1.3, with source-supplied DOI references 10.5281/zenodo.23480803, 10.5281/zenodo.23486020 and 10.5281/zenodo.23318840. These records are research lineage, not independent validation of the proposed models. DOI metadata, formula-domain fit and scientific interpretations require separate external checking. Research Gates & Quality Assurance Boundary The supplied batch QA reports ten 777-page PDFs and no exact duplicate Gate questions or complete six-part annotations. Lexical nearest-neighbor analysis reported a maximum score of 0.8598, with 42 matches at or above 0.80 and none at or above 0.90. Automated checks do not establish independent mechanisms, empirical novelty or clinical safety; specialized review and prospective testing remain outstanding. Join the STARLI Research Community Explore more than 100 interdisciplinary research themes and follow the evolving STARLI Research Gate community: https://zenodo.org/communities/starli-research-gate/ We do not need to know which door future generations will choose to open. We only hope that when they open it, they will find a little light to guide their way. 🌈🚪✨
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RESEARCH COLLECTION / FLAGSHIPS 41–50 OF 90 Nano-Quantum Skincare Metrology & Regenerative Materials at the Limit STARLI Herbal Skincare | F41–F50 / 90 | Author 33 | Research Design Working Edition v1.1 This ten-volume nonclinical research-design collection explores quantum-material adsorption models, traceable cutaneous bioelectric measurement, photon-fair molecular spectroscopy, optical hydration gradients, multimodal microcirculation proxies, smart hydrogel mechanics, biodegradable nanomaterial safety, inflammatory signaling, epidermal renewal, and cellular mechanosensing. Its claims propose testable methods, not observed human skin regeneration, confirmed cosmetic efficacy or established quantum advantage. Two five-theme research units contain ten English 777-page editions: 300 conditional formula plates, 820 chapter openings, 4,100 separately paginated investigations, 2,000 Research Gates and 330 Phoenix Answer Embryos. The v1.1 revision focuses on the preface, formula and Gate pages; chapter and Phoenix content was carried forward. Structural counts and lexical difference scores are not proof of physical validity or scientific originality. Research Unit I · F41–F45 F41 QUANTUM MATERIALS SIMULATION FOR SMART SKINCARE CARRIER DESIGN AT THE LIMIT — Tests facet-dependent adsorption energies against blind facet and solvent-model perturbations. Classical adsorption references and independent surface chemistry distinguish proposed electronic-structure effects from roughness or solvent confounding; unbounded model error blocks skin-performance inference. F42 QUANTUM SENSING AND CUTANEOUS BIOELECTRIC METROLOGY AT THE LIMIT — Measures weak magnetic-field error in traceable skin-like phantoms at equal bandwidth, shielding, time and energy. A qualified classical magnetometer challenges quantum-sensing claims; uncalibrated references or shared sensor interference invalidate apparent gains. F43 QUANTUM-ENHANCED SPECTROSCOPY AND SKIN MOLECULAR ANALYSIS AT THE LIMIT — Challenges blinded analyte-identification rates and detection limits under matched incident-photon and acquisition budgets. Classical Raman and LC-MS benchmarks distinguish molecular specificity from extra light exposure; photodamage and false-positive identification are hard refusals. F44 NANO-OPTICAL SENSING AND SKIN HYDRATION MONITORING AT THE LIMIT — Evaluates optical reconstruction of depth-resolved hydration in layered phantoms with concealed pigmentation and thickness changes. Gravimetry and standard NIR references challenge pigment absorption or surface reflection masquerading as hydration; layer miscalibration and thermal damage block claims. F45 SKIN MICROCIRCULATION AND NONINVASIVE MULTIMODAL METROLOGY AT THE LIMIT — Examines agreement of perfusion proxies across independent instruments during blinded temperature and position crossovers. Calibrated laser Doppler and microfluidic flow references expose correlated temperature artifacts; shared confounding and uncalibrated instruments defeat a positive result. SECOND RESEARCH UNIT / TISSUE SAFETY, TURNOVER & MECHANOBIOLOGY Research Unit II · F46–F50 F46 SMART HYDROGELS AND SKIN TISSUE ENGINEERING AT THE LIMIT — Requires both cell viability and elastic-modulus acceptance under equal hydration and blinded mechanical loading. An inert hydrogel and calibrated scaffold separate water-driven appearance from load-bearing tissue competence; cell injury or gel rupture triggers refusal. F47 BIODEGRADABLE NANOMATERIALS AND CUTANEOUS SAFETY AT THE LIMIT — Tests degradation-product mass balance and cellular compatibility using blind product identification and solvent-only controls. Gravimetric closure and chemically identified standards distinguish true breakdown from simple particle fragmentation; unknown products or toxic intermediates cannot be ignored. F48 CUTANEOUS INFLAMMATORY SIGNALING AND BARRIER HOMEOSTASIS AT THE LIMIT — Tracks cell-stratified inflammatory-marker changes with independent protein assays. Composition-matched qPCR and cytokine verification test whether cell-mixture shifts mimic signaling change; increasing inflammation or loss of barrier integrity invalidates favorable interpretation. F49 EPIDERMAL RENEWAL KINETICS AND SKIN DIFFERENTIATION AT THE LIMIT — Measures labeled-cell transit from basal layers to the surface with blinded time tracking and independent histology. Unperturbed reconstructed epidermis tests whether apparent rapid renewal is merely increased surface-cell shedding; cell death and lost progenitor identity are vetoes. F50 SKIN MECHANOSENSING AND MATRIX REMODELING AT THE LIMIT — Tests mechanical-strain thresholds for cellular signals under isothermal loading and sham challenges. Calibrated direct mechanical references distinguish mechanotransduction from heating-related signals; membrane injury or temperature outside the calibrated range prevents a positive claim. Physical Scope, Falsifiability & Evidence Status The studies are prospective and nonclinical, using skin-like phantoms, traceable materials, authorized reconstructed tissue, controlled cell systems, synthetic references and laboratory benches. Each proposed claim requires an independently measured endpoint, a resource-matched comparator, a rival explanation and a noncompensatory stop condition. Negative trials, unresolved identities and uncertainties remain reportable. No result establishes treatment efficacy, human skin regeneration or safety of a finished cosmetic product. Methodological ancestry in the supplied source includes DOI anchors 10.5281/zenodo.23480803, 10.5281/zenodo.23486020 and 10.5281/zenodo.23318840. These identify research lineage, not experimental verification. Article metadata, claim-specific references and conditional physical equations require independent verification and expert reconciliation before stronger assertions. Research Gate Evolution & QA Boundary The supplied source-batch QA records ten 777-page PDFs and 200 Gates per volume. Automated cross-book checks report zero exactly duplicated Gate questions or full six-part annotations, while four formula pages exceed a 0.80 nearest-neighbor text-similarity threshold. Physical/mathematical, semantic, refutability and DOI reviews remain partial; shared prose persists in unchanged chapters. This delivery repackages the original materials unchanged, without claiming a new scientific audit. Join the STARLI Research Community Explore the evolving STARLI Research Gate community and interdisciplinary research-design collections: https://zenodo.org/communities/starli-research-gate/ We do not need to know which door future generations will choose to open. We only hope that when they open it, they will find a little light to guide their way. 🌈🚪✨
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STARLI / YULI — FAMILY 02 BIOELECTRICITY AND NEURAL REGULATION | TEN FLAGSHIP RESEARCH DESIGNS F011–F015 | FIVE DISTINCT EXPERIMENTS F011 Spatiotemporal Evolution of Acupuncture-Evoked Bioelectric Fields at the Limit Primary endpoint: spatiotemporal voltage gradient (mV/mm). Blinded perturbation: electrode-source delay randomization. Strong baseline / rival: saline-conductive phantom field map; volume conduction masquerading as local source. F012 Peripheral Neural Micro-Signals and Quantum Magnetometry at the Limit Primary endpoint: nerve-phantom magnetic latency error (ms). Blinded perturbation: coded propagation-path lengths. Strong baseline / rival: surface electroneurography; cable leakage appearing as neural propagation. F013 Acupuncture Stimulation and Autonomic Neural Dynamics at the Limit Primary endpoint: beat-interval variability change (ms). Blinded perturbation: randomized simulator pulse trains. Strong baseline / rival: matched rest-and-sham time series; respiration drives the apparent coupling. F014 Bioelectric Signal-Driven Digital Twins for Acupuncture at the Limit Primary endpoint: held-out state prediction RMSE (mV). Blinded perturbation: concealed stimulation waveform shifts. Strong baseline / rival: identified linear state-space model; sensor drift passed as model adaptation. F015 Neural Stimulation Thresholds and Individual Variability at the Limit Primary endpoint: modelled activation threshold (mA). Blinded perturbation: coded electrode geometry swaps. Strong baseline / rival: fixed-threshold biophysical simulator; impedance not neural excitability. FAMILY METHOD / NONCLINICAL BOUNDARY Each 355-page volume has 4 prefaces, 30 candidate formula plates locked to 30 scientific chapters, 100 research gates and 33 Phoenix prototypes. All experiments are proposals for phantoms, permitted ex-vivo systems or simulators; no human intervention, neural efficacy or regeneration is claimed. STARLI / YULI — FAMILY 02 BIOELECTRICITY AND NEURAL REGULATION | TEN FLAGSHIP RESEARCH DESIGNS F016–F020 | FIVE DISTINCT EXPERIMENTS F016 Causal Identification of Acupuncture-Evoked Neural Responses at the Limit Primary endpoint: estimated response contrast (µV). Blinded perturbation: randomized sham-coded stimulus sequence. Strong baseline / rival: prespecified difference-in-differences; operator expectation and measurement bias. F017 Neural Plasticity and Longitudinal Acupuncture Response Tracking at the Limit Primary endpoint: longitudinal response slope (µV/day). Blinded perturbation: simulated delayed washout periods. Strong baseline / rival: no-stimulation trajectory; habituation mistaken for plasticity. F018 Neural Propagation Delays and Acupuncture Timing at the Limit Primary endpoint: conduction delay residual (ms). Blinded perturbation: distance-coded signal launch. Strong baseline / rival: calibrated event-timestamp recorder; trigger jitter mistaken for conduction. F019 Multimodal Neural Sensing and Acupuncture Response Classification at the Limit Primary endpoint: held-out class-balanced accuracy (%). Blinded perturbation: blind sensor-modality ablation. Strong baseline / rival: single-modality classifier; label leakage from stimulus metadata. F020 Safety-Constrained Closed-Loop Acupuncture Neuromodulation at the Limit Primary endpoint: constraint-violation count (events). Blinded perturbation: simulator fault and sensor-delay injection. Strong baseline / rival: fixed conservative controller; feedback delay destabilizes control. EVIDENCE AND SOURCE LINEAGE F29 parent (777p): quantum sensing, traceable metrology and controlled comparison. M1 10.5281/zenodo.23480803; M2 10.5281/zenodo.23486020; M3 10.5281/zenodo.23318840 (authored lineage, not replication). External methods: 10.1103/RevModPhys.89.035002 and 10.59161/JCGM100-2008E. Source claims and external DOIs remain subject to verification. STARLI Research Community: https://zenodo.org/communities/starli-research-gate/
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Machine Learning–Based Prediction of Organic Solar Cell Performance Using Molecular Descriptors
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Computational Fluid Dynamics and Machine Learning Integration for High Performance Mechanical Engineering Thermal Systems Analysis
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A comprehensive simulation study evaluating the predictive performance of Cox proportional hazards model and machine learning methods for time-to-event data
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Learning techniques for the load forecasting of parcel pick-up points
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Un modèle de transition logico-matérielle pour la simplification de la programmation parallèle
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Statistical Learning Methodology to Leverage the Diversity of Environmental Scenarios in Crop Data : Application to the prediction of crop production at large-scale
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The simple multivariable model for predicting liver fibrosis in Vietnamese male adults: a combination of Bayesian model averaging and stepwise method
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Fuel cell health prognosis using Unscented Kalman Filter: Postal fuel cell electric vehicles case study
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Monthly Evapotranspiration Modeling in the Absence of Climatic Data Using the Soft Computing Methods in West and Northwest of Iran
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