Necessary elements of precautionary management: implications for the Antarctic toothfish
Bibliographic record
Abstract
Abstract We review the precautionary approach to fisheries management, propose a framework that will allow a systematic assessment of insufficient precaution and provide an illustration using an Antarctic fishery. For a single‐species fishery, our framework includes five attributes: (1) limit reference points that recognize gaps in our understanding of the dynamics of the species; (2) accurate measures of population size; (3) ability to detect population changes quickly enough to arrest unwanted declines; (4) adequate understanding of ecosystem dynamics to avoid adverse indirect effects; and (5) assessment of the first four elements by a sufficiently impartial group of scientists. We argue that one or more of these elements frequently fail to be present in the management of many fisheries. Structural uncertainties, which characterize almost all fisheries models, call for higher limit points than those commonly used. A detailed look into the five elements and associated uncertainties is presented for the fishery on the Antarctic toothfish in the Ross Sea (FAO/CCAMLR Area 88.1, 88.2), for which management was recently described as ‘highly precautionary’. In spite of having features that make the Ross Sea fishery ideal for the application of the precautionary approach, gaps in our knowledge and failure to acknowledge these gaps mean that current regulation falls short of being sufficiently precautionary. We propose some possible remedies.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".