Peer2ref: a peer-reviewer finding web tool that uses author disambiguation
Bibliographic record
Abstract
BACKGROUND: Reviewer and editor selection for peer review is getting harder for authors and publishers due to the specialization onto narrower areas of research carried by the progressive growth of the body of knowledge. Examination of the literature facilitates finding appropriate reviewers but is time consuming and complicated by author name ambiguities. RESULTS: We have developed a method called peer2ref to support authors and editors in selecting suitable reviewers for scientific manuscripts. Peer2ref works from a text input, usually the abstract of the manuscript, from which important concepts are extracted as keywords using a fuzzy binary relations approach. The keywords are searched on indexed profiles of words constructed from the bibliography attributed to authors in MEDLINE. The names of these scientists have been previously disambiguated by coauthors identified across the whole MEDLINE. The methods have been implemented in a web server that automatically suggests experts for peer-review among scientists that have authored manuscripts published during the last decade in more than 3,800 journals indexed in MEDLINE. CONCLUSION: peer2ref web server is publicly available at http://www.ogic.ca/projects/peer2ref/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.093 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.033 | 0.015 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.046 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".