Implications of uncertainty for Canada’s commercial hunt of harp seals (Pagophilus groenlandicus)
Bibliographic record
Abstract
Abstract The Canadian government's current management procedure for harp seals is described by Fisheries and Oceans Canada as using the Precautionary Approach. Employing a similar underlying population model, we simulated the effects of uncertainty involving bias in estimates of human induced mortality, natural mortality, and pup production estimates as a set of robustness trials. Our results indicated that for the range of annual total allowable catches (TAC) considered and set for Canada’s commercial harp seal hunt (250,000 – 350,000), there were plausible circumstances under which the government's management procedures failed to meet their own conservation objectives. By contrast, a precautionary management regime should be robust to such levels of uncertainty. For some scenarios the current management strategy, although not fully specified, is likely to maintain a high TAC despite a declining population. In particular, once a high TAC has been set, the assessments are unlikely to provide the necessary evidence that the TAC should be reduced until the population is at a low level. Hence there is a substantial risk that the population may be depleted below the ‘minimum’ (N50) and ‘critical’ (N30) population reference points. There is a need for a management procedure based on risk analysis to be fully specified and tested. In the interim, reducing TACs to within limits calculated from a well-established precautionary procedure, such as Potential Biological Removal, would be a step towards more precautionary management.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".