Introducing a special issue with a focus on systematic reviews
Bibliographic record
Abstract
Abstract Systematic reviews answer specific review questions by following structured steps and employing specific methods to reduce the risk of bias and to maximize transparency in the process of the review, and systematic review methodology differs from traditional narrative reviews in many ways. As a journal devoted to reviews, it is appropriate that Animal Health Research Reviews (AHRR) includes this approach to reviews of the literature. The aim of this special issue of AHRR was to illustrate the scope of articles that can be considered for submission to the systematic review section of this journal for prospective authors and readers.
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 | 0.032 | 0.141 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.026 | 0.030 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".