The Canadian harp seal hunt: observations on the effectiveness of procedures to avoid poor animal welfare outcomes
Bibliographic record
Abstract
Abstract The Canadian harp seal(Pagophilus groenlandicus)hunt has, for several decades, raised public concerns related to animal welfare. The field conditions under which this hunt is carried out do not lend themselves easily to detailed observations and analyses of its killing practices. This article reports observations carried out over several seasons that aimed at obtaining more specific information about the conditions under which seals are killed, in order to assess potential welfare issues and explore avenues for possible improvements in its practice. A standardised three-step process for killing seals (ie stunning, checking by palpation of the skull, and bleeding) was recently implemented to maximise the proportion of animals that are killed rapidly with minimum pain. Based on field observations, the rifle and the hakapik, when used properly, appeared to be efficient tools for stunning and/or killing young harp seals. All carcases of seals observed to be killed with a rifle, either on the ice or in the water, could be recovered. However, shooting seals in water rather than on ice carried a higher risk of poor welfare outcome because of the limited opportunities to shoot the animals again if not stunned with the first shot. Based on current practices, there is no reliable evidence that the Canadian harp seal hunt differs from other forms of exploitation of wildlife resources from the perspective of animal welfare. Although opportunistic field observations may be less amenable to generalisation than structured studies, we believe that they reflect the reality of the hunt and provide valuable information to direct the evolution of its practice.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".