Comparison of effects of different methods of culling red deer<i>(Cervus elaphus)</i>by shooting on behaviour and post mortem measurements of blood chemistry, muscle glycogen and carcase characteristics
Bibliographic record
Abstract
Abstract Methods for culling wild red deer(Cervus elaphus)were compared by observing behaviour and collecting post mortem samples from wild deer shot: (i) by a single stalker during daytime; (ii) by more than one stalker during daytime; (iii) by using a helicopter for the deployment of stalkers and carcase extraction; or (iv) by a single stalker at night, and compared with farmed red deer shot in a field or killed at a slaughterhouse. Culling by a single stalker during the day and shooting in a field were the most accurate in achieving placement of a shot in a target area, but when compared across all methods, there were no significant differences in the percentages of deer that were either wounded or appeared to have died immediately after the first shot. Plasma cortisol concentrations in deer shot using helicopter assistance were similar to those in deer at the slaughterhouse, but higher than deer shot at night or during the day by a single stalker, or in a field. Deer shot using helicopter assistance and also deer culled by a collaborative and single stalking during the day had lower muscle glycogen concentrations than those culled by a single stalker at night. There was no evidence that a particular culling method was associated with an increased risk of accidental or pre-culling injury. If a helicopter is used to assist culling, the deer are more likely to be disturbed before they are shot and therefore, measures should be taken to minimise the disturbance to the deer.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".