Bibliographic record
Abstract
Abstract Fisheries management is the set of science‐based procedures used by government institutions to regulate fishers' access to fisheries resources; this involves temporal and spatial restrictions on the deployment of fishing gear, restrictions on features of these gear and constraints on the species and size composition of the catch, and its overall magnitude. The traditional goal of fisheries management was to achieve maximum sustainable yields. Maximum economic yields are obtained with slightly lower catches from larger fish stocks. Modern fisheries management aims for minimising the impact of fishing on the ecosystem and considers trophic interactions when determining catch levels. A new challenge is the assessment and management of data‐limited fish stocks, which constitute about three‐fourth of the exploited stocks. Key Concepts: Fish stocks must be maintained above levels that allow them to produce the maximum sustainable yield. Mortality caused by fishing may not exceed the rate of mortality from natural causes such as predation, diseases or old age. The size at first capture must be chosen such that fish can realise their potential for growth and reproduction. Species with important ecosystem functions, such as forage fish, must be fished less. Government subsidies to fisheries, by reducing the cost of fishing, allows fishing to continue even when fish stocks are depleted; reducing subsides to fisheries thus contributes to fishery sustainability. Aquaculture can contribute to the global fish supply, but not when carnivorous fish are farmed, as they consume more fish than they produce.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.163 | 0.067 |
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".