Preprints in comparative physiology – a guide for the preprint curious
Bibliographic record
Abstract
The deposition of pre-peer-reviewed scientific articles in repositories as preprints has been practised for over 50 years. Recently, the popularity of this practice has surged, particularly in chemistry and physics disciplines. In the life sciences, bioRxiv is a popular preprint server; however, its usage varies greatly between fields. Preprinting is not common practice within comparative physiology, with the number of manuscripts submitted lagging far behind that seen in other fields. In this Perspective, we dig into the possible explanations for this difference. We explore common concerns regarding the deposition and use of preprints and highlight some relevant reasons why preprints are helpful to the field of comparative physiology. We strongly believe that use of preprints can help to improve transparency in the scientific publishing process and will be an important component of publishing for all fields of science in the future.
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 | 0.039 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.175 | 0.294 |
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".