Towards a precautionary approach to managing Canada's commercial harp seal hunt
Bibliographic record
Abstract
Abstract Leaper, R., Lavigne, D. M., Corkeron, P., and Johnston, D. W. 2010. Towards a precautionary approach to managing Canada's commercial harp seal hunt. – ICES Journal of Marine Science, 67: 316–320. The Canadian government's approach to the management of its commercial harp seal hunt is compared with other precautionary approaches developed for setting anthropogenic removal limits for marine mammal populations. For Canada's harp seal hunt, the current management strategy has not been fully specified or tested, and its robustness to changes in biological parameters, uncertainty in input data and environmental variability, remains unknown. As such, the management approach cannot be considered precautionary and there is a substantial, but not quantified, probability that it will not meet its objectives. There is an urgent need for a fully specified and rigorously tested management procedure, and steps towards this are suggested that should reduce the risks associated with the current approach.
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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".