Bibliographic record
Abstract
The Wiley-Blackwell Fish and Aquatic Resources Series is an initiative aimed at providing key books in this fast-moving field, published to a high international standard.The Series includes books that review major themes and issues in the science of fishes and the interdisciplinary study of their exploitation in human fisheries.Volumes in the Series combine a broad geographical scope with in-depth focus on concepts, research frontiers, and analytical frameworks.These books will be of interest to research workers in the biology, zoology, ichthyology, ecology, and physiology of fish and the economics, anthropology, sociology, and all aspects of fisheries.They will also appeal to non-specialists such as those with a commercial or industrial stake in fisheries.It is the aim of the editorial team that books in the Wiley-Blackwell Fish and Aquatic Resources Series should adhere to the highest academic standards through being fully peer reviewed and edited by specialists in the field.The Series books are produced by Wiley-Blackwell in a prestigious and distinctive format.The Series Editor, Professor Tony J. Pitcher, is an experienced international author, and founding editor of the leading journal in the field, Fish and Fisheries.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.910 | 0.883 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".