Recoverable Identifier

arXiv:2604.20833 · detector doi_compliance · incontrovertible · 2026-05-20 01:33:01.649275+00:00

advisory doi_compliance recoverable_identifier

DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1007/978-3-031-72215-8_12.url:https://doi.org/10.1007/978-3-031-72215-8_12) 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

Ramatoulaye Diallo, Codjo Edalo, and O. Olawale Awe. “Machine Learning Evaluation of Im- balanced Health Data: A Comparative Analysis of Balanced Accuracy, MCC, and F1 Score”. In: Practical Statistical Learning and Data Science Methods: Case Studies from LISA 2020 Global Network, USA. Ed. by O. Olawale Awe and Eric A. Vance. Cham: Springer Nature Switzerland, 2025, pp. 283–312.isbn: 978-3-031-72215-8.doi:10.1007/978-3-031-72215-8_12.url:https: //doi.org/10.1007/978-3-031-72215-8_12

Evidence payload

{
  "printed_excerpt": "Ramatoulaye Diallo, Codjo Edalo, and O. Olawale Awe. \u201cMachine Learning Evaluation of Im- balanced Health Data: A Comparative Analysis of Balanced Accuracy, MCC, and F1 Score\u201d. In: Practical Statistical Learning and Data Science Methods: Cas",
  "reconstructed_doi": "10.1007/978-3-031-72215-8_12.url:https://doi.org/10.1007/978-3-031-72215-8_12",
  "ref_index": 56,
  "resolved_title": null,
  "verdict_class": "incontrovertible"
}