Developments in the Quantitative Assessment of Welfare Outcomes in Hunted Mammals Subject to Shooting
Bibliographic record
Abstract
Knowledge gaps surrounding animal welfare assessment in hunted terrestrial wild mammals and seals were highlighted in the reviews by Knudsen (2005) and EFSA (2007). Following these reviews, the present paper aims to report on developments in the quantitative assessment of welfare outcomes in wild mammals killed via rifle shooting, and modern explosive harpoon grenades used in the killing of whales. Time to death (TTD) and instantaneous death rate (IDR) are widely accepted ante-mortem variables for assessing the duration of suffering during the killing process. The addition of post-mortem assessments allows for validation of TTD and IDR, thus providing a more accurate appraisal of animal welfare during hunting. While this combined assessment for large cetaceans has been implemented since the 1980s in the Norwegian minke whale (Balaenoptera acutorostrata) hunt, we report that this approach has been implemented in studies of the Icelandic minke and fin whale (Balaenoptera physalus) hunts, as well as the Canadian and Norwegian commercial harp seal (Pagophilus groenlandicus) hunts. Additionally, this approach has been incorporated into welfare studies in terrestrial herbivore management programmes. Quantitative welfare assessment during hunts is capable of effectively evaluating the weapons used and judging modifiable variables such as projectile choice, optimal shooting procedure, as well as identifying areas for improvement in hunter training. In moving towards a standardised approach for welfare outcome assessment, an established framework can effectively allow all hunts to be contrasted and allow for identification of optimal strategies that minimise animal suffering.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".