Recoverable Identifier

arXiv:2604.19223 · detector doi_compliance · incontrovertible · 2026-05-20 03:14:02.479790+00:00

advisory doi_compliance recoverable_identifier

DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.3390/e20030201accessed:Dec) 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

R. Preuss and U. Von Toussaint, “Global Optimization Employing Gaussian Process-Based Bayesian Surrogates,” en,Entropy, vol. 20, no. 3, p. 201, Mar. 2018, Number: 3 Publisher: Multidisciplinary Digital Publishing Institute.doi: 10.3390/e20030201Accessed: Dec. 1, 2020. [Online]. Available:https://www. mdpi.com/1099-4300/20/3/201

Evidence payload

{
  "printed_excerpt": "R. Preuss and U. Von Toussaint, \u201cGlobal Optimization Employing Gaussian Process-Based Bayesian Surrogates,\u201d en,Entropy, vol. 20, no. 3, p. 201, Mar. 2018, Number: 3 Publisher: Multidisciplinary Digital Publishing Institute.doi: 10.3390/e200",
  "reconstructed_doi": "10.3390/e20030201accessed:Dec",
  "ref_index": 41,
  "resolved_title": null,
  "verdict_class": "incontrovertible"
}