Trust, but Verify-Post-Hoc Analysis of Industrial Machine Learning via Interpretability Metric Embedding and Surrogate Mapping.
- DOI
- 10.3390/s26103232
- Published
- 2026 May 20
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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.
Cite this work
BibTeX
@article{allodium:10.3390/s26103232,
title = {Trust, but Verify-Post-Hoc Analysis of Industrial Machine Learning via Interpretability Metric Embedding and Surrogate Mapping.},
author = {Mählkvist S and Netzell P and Helander T and Kyprianidis K},
year = {2026},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s26103232},
url = {https://doi.org/10.3390/s26103232}
}RIS
TY - JOUR TI - Trust, but Verify-Post-Hoc Analysis of Industrial Machine Learning via Interpretability Metric Embedding and Surrogate Mapping. AU - Mählkvist S AU - Netzell P AU - Helander T AU - Kyprianidis K PY - 2026 JO - Sensors (Basel, Switzerland) DO - 10.3390/s26103232 UR - https://doi.org/10.3390/s26103232 ER -
APA
S, M., P, N., T, H., & K, K. (2026). Trust, but Verify-Post-Hoc Analysis of Industrial Machine Learning via Interpretability Metric Embedding and Surrogate Mapping.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26103232
Source records
- pubmed · retrieved 2026-09-26T22:29:26.454Z