Bibliographic record
Abstract
Although rarely considered predators, wildlife hunters can function as important ecological and evolutionary agents. In part, their influence relates to targeting of large reproductive adults within prey populations. Despite known impacts of size-selective harvests, however, we know little about what enables hunters to kill these older, rarer, and presumably more wary individuals. In other mammalian predators, predatory performance varies with knowledge and physical condition, which accumulates and declines, respectively, with age. Moreover, some species evolved camouflage as a physical trait to aid in predatory performance. In this work, we tested whether knowledge-based faculty (use of a hunting guide with accumulated experience in specific areas), physical traits (relative body mass [RBM] and camouflage clothing), and age can predict predatory performance. We measured performance as do many hunters: size of killed cervid prey, using the number of antler tines as a proxy. Examining ∼ 4300 online photographs of hunters posing with carcasses, we found that only the presence of guides increased the odds of killing larger prey. Accounting for this effect, modest evidence suggested that unguided hunters presumably handicapped with the highest RBM actually had greater odds of killing large prey. There was no association with hunter age, perhaps because of our coarse measure (presence of grey hair) and the performance trade-offs between knowledge accumulation and physical deterioration with age. Despite its prevalence among sampled hunters (80%), camouflage had no influence on size of killed prey. Should these patterns be representative of other areas and prey, and our interpretations correct, evolutionarily-enlightened harvest management might benefit from regulatory scrutiny on guided hunting. More broadly, we suggest that by being nutritionally and demographically de-coupled from prey and aided by efficient killing technology and road access, wildlife hunters in the developed world might have overcome many of the physical, but not knowledge-based, challenges of hunting.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".