An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process
- DOI
- 10.48550/arxiv.2603.13584
- Published
- 2026
- Container
- Not recorded
- Publisher
- arXiv
- Open access
- yes
Credibility signals
limited evidence Score 43/100 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.
- supportingPublication license: A publication license was supplied: https://creativecommons.org/licenses/by/4.0/legalcode. Presence improves reuse transparency, not research validity.
- cautionPublication version: Identified as a preprint; peer review and later versions may change the record.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.48550/arxiv.2603.13584,
title = {An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process},
author = {Synovic, Nicholas M. and Ryzka, Karolina and Solari, Alessandra V. Vellucci and Lyons, Kenny and Davis, James C. and Thiruvathukal, George K.},
year = {2026},
doi = {10.48550/arxiv.2603.13584},
url = {https://doi.org/10.48550/arxiv.2603.13584}
}RIS
TY - JOUR TI - An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process AU - Synovic, Nicholas M. AU - Ryzka, Karolina AU - Solari, Alessandra V. Vellucci AU - Lyons, Kenny AU - Davis, James C. AU - Thiruvathukal, George K. PY - 2026 DO - 10.48550/arxiv.2603.13584 UR - https://doi.org/10.48550/arxiv.2603.13584 ER -
APA
M., S. N., Karolina, R., Vellucci, S. A. V., Kenny, L., C., D. J., & K., T. G. (2026). An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process. https://doi.org/10.48550/arxiv.2603.13584
Source records
- datacite · retrieved 2026-09-25T16:35:37.340Z