Thank You to Our GeoHealth 2023 Reviewers
Bibliographic record
Abstract
Abstract Peer‐review is the foundation and the safeguard of scientific research. Without the dedication of our reviewers, the journal would not have been successful. In 2023, 269 reviewers completed 434 reviews for the 174 manuscripts submitted to GeoHealth. Our reviewers are from all continents except Antarctica. Besides reviewers from North America, China, Europe, and China, we started to have reviewers from India, Latin America, and Africa. GeoHealth editorial board is committed to expanding the readership, authorship, and reviewership to other countries. If you have already reviewed for us, no matter where or who you are, we hope you and your colleagues will consider GeoHealth a home for your work. Below is the list of reviewers who completed more than two reviews or have outstanding quality reviews. Two of our reviewers are being nominated for AGU best reviewers awards.
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 | 0.036 | 0.310 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.099 | 0.146 |
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".