Assessment of current and alternative methods for killing young grey seals (Halichoerus grypus) during commercial harvest
Bibliographic record
Abstract
A small commercial hunt for 5-7 weeks old grey seals (Halichoerus grypus) occurs intermittently\naround the Canadian Maritime provinces and may expand in the near future. We sought to\nbetter understand and, where possible, improve the harvesting methods of this hunt. We\ncompared the use of the regulation club and the regulation Canadian hakapik to effectively\ncrush the skull of these animals under field conditions. Both tools achieved this purpose,\nresulting in rapid, if not immediate, death of the animals, but a difference approaching\nsignificance was detected which suggested that the club requires fewer blows than the hakapik\nto crush the skulls. We also tested the efficacy\nof the .17 HMR (Hornady Magnum Rimfire) rifle\ncartridge, an ammunition of low energy but high velocity, to quickly kill seals of this age at close\nrange with a shot to the head. All 12 animals studied under controlled conditions and 40 of 45\n(88.9%) animals studied under field conditions died immediately or within a few seconds from a\nsingle shot. We believe that the latter proportion can be increased further with simple\nmodifications to the method used during the field study and that the .17 HMR rifle cartridge can\nbe an effective tool to quickly kill young grey seals during a commercial hunt, as a possible\nsubstitute to the use of the club or hakapik.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".