Recoverable Identifier

arXiv:2605.15874 · detector doi_compliance · incontrovertible · 2026-05-20 20:44:36.903554+00:00

advisory doi_compliance recoverable_identifier

DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1038/s41598-025-08436-x.https://doi.org/10.1038/s41598-025-08436-x) was visible in the surrounding text but could not be confirmed against doi.org as printed.

Paper page Integrity report arXiv Try DOI

Evidence text

Aly, M., Behiry, M.H., 2025. Enhancing anomaly detection in iot-driven factories using logistic boosting, random forest, and svm: A comparative machine learning approach. Scientific Re- ports 15. doi:10.1038/s41598-025-08436-x.https://doi.org/10.1038/ s41598-025-08436-x

Evidence payload

{
  "printed_excerpt": "Aly, M., Behiry, M.H., 2025. Enhancing anomaly detection in iot-driven factories using logistic boosting, random forest, and svm: A comparative machine learning approach. Scientific Re- ports 15. doi:10.1038/s41598-025-08436-x.https://doi.o",
  "reconstructed_doi": "10.1038/s41598-025-08436-x.https://doi.org/10.1038/s41598-025-08436-x",
  "ref_index": 4,
  "resolved_title": null,
  "verdict_class": "incontrovertible"
}