Behavioural responses of southern mountain caribou to helicopter and skiing activities
Bibliographic record
Abstract
Helicopter- and snowcat-supported backcountry skiing is a unique industry that is widespread throughout southern mountain caribou habitat in British Columbia. We analyzed standardized caribou encounter data collected by operators under an agreement between the BC government and Helicat Canada. Average reported encounter rates were low for helicopters (0.6%) and skiing groups (0.1%); however, encounters were likely underreported due to factors that affect caribou sightability. Although helicopters encountered caribou more frequently than skiing groups, caribou were detected from helicopters at greater distances than by skiers. We used Bayesian network models to assess the independent contribution of different factors to the behavioural response of caribou to encounters. Encounter distance was the most important factor in both helicopter and skiing models. Larger groups of caribou responded strongly to skiers but not to helicopters, although the independent effect of this factor was small in both models. Larger helicopters elicited stronger reactions from caribou than smaller machines and was responsible for 25% of the modelled variation in caribou response. Encounters with helicopters at distances of 100–500m had a 78% probability of eliciting a concerned-to-very-alarmedresponse from caribou, while skiers at a similar distance had a 60% probability of eliciting the same response. The probability of concerned-to-very-alarmed responses dropped to <20% at encounter distances of >1000 m. These results indicate that initial encounter distance is the key variable to manage risk to caribou of helicopter and skiing encounters. Ongoing feedback on the effectiveness of management practices is critical to ensure the continued viability of industries operating in caribou habitat.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".