Caribou, military jets and noise: The interplay of behavioural ecology and evolutionary psychology
Bibliographic record
Abstract
Whether a human activity is likely to have a negative impact on a species depends largely on how stimuli from that activity are interpreted and acted upon by individuals, within the context of their behavioural ecology. The interpretations and decisions made by individuals in response to these stimuli are largely governed by neural systems evolved by the species as adaptations to common and recurrent selective pressures. In this paper I will review previous findings concerning the responses of caribou to overflights by military jet aircraft in Labrador/Quebec and Alaska, casting them in an evolutionary psychological framework. One prediction from such an exercise is that identical stimuli (noise from jet overflights) that elicit similar responses (short-distance avoidance) can have quite different population consequences for sedentary (woodland) and migratory (barren-ground) ecotypes. For a female woodland caribou, which shares her calving range with a resident predator population, an increase in movements following disturbance may significantly increase her calf's exposure to predators. Similar movements by a female barren-ground caribou, which has fewer predators to contend with, may have only a negligible impact on her calf's predation risk. Thus woodland caribou may be more vulnerable to negative impacts of military jet noise during calving periods, dependent on predator density.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".