A framework for evaluating management plans comprehensively
Bibliographic record
Abstract
Abstract We present a framework for evaluating fisheries management plans comprehensively, both rebuilding plans and others. The framework includes a first rapid appraisal of the likelihood that the plan will result in management meeting its objectives, and guides subsequent quantitative analyses of potential weaknesses in the proposed plan. The framework includes four steps: (i) evaluating if a set of management objectives, if achieved, would result in a sustainable fishery, (ii) using qualitative analysis of a bio‐economic model to evaluate whether the set of stock management tactics might be capable of achieving the specified fisheries objectives, (iii) using empirical criteria derived from the literature to evaluate if other management measures in the plan related to the ecological, social or economic context of the fishery actually contribute to sustainability, and (iv) carrying out quantitative simulations to compare alternative implementation options. Generally, several management measures have to be combined to increase stock size without sacrificing the economic benefits to the fishers remaining in the fishery. We demonstrate application of the framework for evaluating the stock rebuilding plan for plaice (Pleuronectes platessa) and sole (Solea solea) in the North Sea and, the management measures currently in place for the roundnose grenadier (Coryphaenoides rupestris) stock exploited to the west of the British Isles.
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 | 0.044 | 0.067 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".