Possible solutions to some challenges facing fisheries scientists and managers
Bibliographic record
Abstract
Abstract The purpose of this paper is to review recent work on four key challenges in fisheries science and management: (1) dealing with pervasive uncertainties and risks; (2) estimating probabilities for uncertain quantities; (3) evaluating performance of proposed management actions; and (4) communicating technical issues. These challenges are exacerbated in fisheries that harvest multiple stocks, and various methods provide partial solutions to them: (i) risk assessments and decision analyses take uncertainties into account by permitting several alternative hypotheses to be considered at once. (ii) Hierarchical models applied to multi-stock data sets can improve estimates of probability distributions for model parameters compared with those derived through single-stock analyses. (iii) Operating models of complete fishery systems provide comprehensive platforms for testing management procedures. (iv) Finally, results from research in such other disciplines as cognitive psychology can facilitate better communication about uncertainties and risks among scientists, managers, and stakeholders.
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 | 0.081 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.029 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".