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10 merged results for "AGENTS Journal of Artificial Intelligence and Data Science"

Partial results: at least one source did not answer. Available results are shown rather than treating an upstream outage as zero matches.

1. Making waves: Human-governed workflow logic for agentic AI in water management.

   Wang Z, Fu G, Zhang C · 2026 · Water research

   limited evidence Transparent signal score 43/100 · policy 1.0.0

   Found in pubmed · DOI 10.1016/j.watres.2026.126968

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   • cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
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   • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
   • 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. Source: Retraction Watch; license: CC BY 4.0
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   • not scoredOpen access status: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
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   • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

2. Tirzepatide versus GLP-1 receptor agonists and stroke in patients with atrial fibrillation and type 2 diabetes.

   Lin CL, Lin KC, Yu JM, Chang CL · 2026 · Nature communications

   limited evidence Transparent signal score 45/100 · policy 1.0.0

   Found in pubmed · DOI 10.1038/s41467-026-77047-5

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   • cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
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   • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
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   • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

3. Human learning is an understudied but promising lever for boosting human-AI synergy.

   Berger J, Burton JW, Hertwig R, Kosch T · 2026 · Proceedings of the National Academy of Sciences of the United States of America

   limited evidence Transparent signal score 43/100 · policy 1.0.0

   Found in pubmed · DOI 10.1073/pnas.2536100123

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   • cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
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   • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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   • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

4. Real-Time Precision Psychiatry for Schizophrenia: A Microfluidic Genotyping and Super-Learner Platform for Treatment Response Prediction.

   Li JQ, Tang T, Yu TG, Wang Q · 2026 · Schizophrenia bulletin

   limited evidence Transparent signal score 43/100 · policy 1.0.0

   Found in pubmed · DOI 10.1093/schbul/sbag044

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   • cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
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   • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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   • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
   • not scoredOpen access status: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
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   • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
   • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

5. Deciphering the Dysregulated Pathways and Candidate Therapeutic Compounds for Primary Ovarian Cancer Using Whole Transcriptomics Data and Next Generation Knowledge Discovery Strategies.

   Pushparaj PN, Gauthaman K, Alahmadi AG, Hassan RN · 2026 · Journal of cellular and molecular medicine

   limited evidence Transparent signal score 45/100 · policy 1.0.0

   Found in pubmed · DOI 10.1111/jcmm.71356

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   • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
   • 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. Source: Retraction Watch; license: CC BY 4.0
   • not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
   • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
   • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
   • not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
   • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
   • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

6. Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria.

   Krentzel D, Petit J, Boudehen YM, Mahtal N · 2026 · Science advances

   limited evidence Transparent signal score 43/100 · policy 1.0.0

   Found in pubmed · DOI 10.1126/sciadv.aeg6806

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   • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
   • 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. Source: Retraction Watch; license: CC BY 4.0
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   • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
   • not scoredOpen access status: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
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   • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
   • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

7. How generative AI shapes the last meal at life's end.

   Shirotsuki M, Otsuki S, Oiwa S, Sugama J · 2026 · Nursing ethics

   limited evidence Transparent signal score 43/100 · policy 1.0.0

   Found in pubmed · DOI 10.1177/09697330261483891

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   • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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   • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
   • not scoredOpen access status: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
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   • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
   • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

8. In-silico personalized protein-protein interaction networks prioritize candidate compounds for glioblastoma.

   Siminea N, Popescu VB, Păun M, Petre I · 2026 · PLoS computational biology

   limited evidence Transparent signal score 45/100 · policy 1.0.0

   Found in pubmed · DOI 10.1371/journal.pcbi.1014785

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   • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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   • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
   • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
   • not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
   • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
   • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

9. Machine learning models in predicting antimicrobial resistance in gonorrhea: a systematic review and meta-analysis.

   Chinaecherem Innocent D, Chijindum Innocent R, Praise Innocent I · 2026 · Frontiers in public health

   limited evidence Transparent signal score 45/100 · policy 1.0.0

   Found in pubmed · DOI 10.3389/fpubh.2026.1894150

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   • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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   • not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
   • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
   • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
   • not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
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   • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

10. Unlocking Sensitive Data with SPHERE in the Age of AI.

    He Z, Park J, Pulgrossi RC, Lee J · 2026 · bioRxiv : the preprint server for biology

    limited evidence Transparent signal score 45/100 · policy 1.0.0

    Found in pubmed · DOI 10.64898/2026.09.01.748580

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    • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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    • not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
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    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer