HeliCat Canada’s Wildlife Observations Program: Trends and Findings 2012-2022
Bibliographic record
Abstract
HeliCat Canada members operating in mountain caribou range have been collecting and reporting wildlife sightings data since 2010, resulting in more than 3,750 spatially referenced sightings of animals and tracks by the end of the 2022 operating season. Mountain caribou, mountain goats, and wolverine have been the most commonly recorded species, with caribou observations generally declining over time, mountain goat sightings showing no trend, and observations of wolverines and their tracks increasing. No reaction and Unconcerned are the most common responses of caribou and mountain goats to encounters with helicopters or skiers. Behavioural responses are generally stronger at shorter encounter distances, but responses remain variable, with some animals still showing No reaction or an Unconcerned response to close encounters. Recorded responses to skiers tend to be stronger than to helicopters, particularly among caribou. The number and proportion of Alarmed or Very alarmed responses to encounters was higher in early years of monitoring but has been stable since about 2014. Evidence suggesting that HeliCat activities are causing population declines of mountain caribou or mountain goats is lacking. Opportunities to further reduce risk include: improved habitat mapping, expanded monitoring and reporting, telemetry data sharing, and passive detection of wildlife. However, the benefits of all of these approaches are also currently associated with a variety of limitations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".