Animal welfare testing for shooting and darting free-ranging wildlife: a review and recommendations
Bibliographic record
Abstract
Several important techniques for managing wildlife rely on ballistics (the behaviour of projectiles), including killing techniques (shooting) as well as capture and marking methods (darting). Because all ballistic techniques have the capacity to harm animals, animal welfare is an important consideration. Standardised testing approaches that have allowed refinement for other physical killing and capture methods (e.g. traps for mammals) have not been applied broadly to ballistic methods. At the same time, new technology is becoming available for shooting (e.g. subsonic and lead-free ammunition) and darting (e.g. dye-marker darts). We present several case studies demonstrating (a) how basic ballistic testing can be performed for novel firearms and/or projectiles, (b) the benefits of identifying methods producing undesirable results before operational use, and (c) the welfare risks associated with bypassing testing of a technique before broad-scale application. Following the approach that has been used internationally to test kill-traps, we suggest the following four-step testing process: (1) range and field testing to confirm accuracy and precision, the delivery of appropriate kinetic energy levels and projectile behaviour, (2) post-mortem assessment of ballistic injury in cadavers, (3) small-scale live animal pilot studies with predetermined threshold pass/fail levels, and (4) broad-scale use with reporting of the frequency of adverse animal welfare outcomes. We present this as a practical approach for maintaining and improving animal welfare standards when considering the use of ballistic technology for wildlife 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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".