Annual re-habituation of calving caribou to oilfields in northern Alaska: implications for expanding development
Bibliographic record
Abstract
Previous research led to hypotheses that calving caribou ( Rangifer tarandus granti J.A. Allen, 1902) in north Alaskan oilfields habituated to human activities: (i) across years and (ii) annually after spring migration (i.e., re-habituation). We used predictor variables of year and a spring snowmelt index to evaluate weight of evidence for these competing hypotheses. Response variables were calf percentage and sighting rate of calving caribou along a high-traffic road system from 1982 to 1990 and 2000 to 2002. We also considered local calf percentage and caribou density, determined by aerial surveys, for respective response variables. We found no evidence of habituation across years. We found two more lines of evidence (one strong and one weaker) for re-habituation within years during calving periods. Post hoc models suggested a further tolerance response exhibited by caribou; more data are needed. Even when snow melted early and calving caribou were most habituated among years, caribou and calves were under-represented near the road system. Investigation of a traffic-rate effect seems warranted. However, habitat selection and forage availability should be considered when interpreting avoidance behaviour at a larger spatial extent. We contend that the behavioural adaptability of calving caribou exhibited in existing oilfields was contingent on the no-hunt policy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".