Behaviours of moose at roadside mineral licks in British Columbia: Implications for moose-vehicle collisions
Bibliographic record
Abstract
Moose (Alces americanus) visit roadside mineral licks (RMLs; areas of roadside ditches where de-icing salts accumulate in spring) to obtain minerals that may be otherwise lacking in their diet. When moose use road corridors to access salts, they become hazards to motorists. Moose use of RMLs is dependent on season and time of day, but specific patterns of use and associated behaviours that may influence moose-vehicle collision risk are unknown. We used video-enabled camera traps, analysis of variance, and generalized additive mixed models to record, review, interpret, classify, and analyze the behaviours of adult moose between July 2012—July 2020 at five RMLs in north-central British Columbia. Monthly visitation rates to RMLs peaked in mid-summer which corresponds with the summer peak in moose-vehicle collisions in the study area. Bi-hourly visitation rates peaked at night. Vigilance and licking were the most common of many behaviours recorded. Cows with young spent the most time at RMLs, followed by bulls, then solitary cows. Proportion of time spent vigilant peaked in May, licking peaked in June. Time spent licking was highest for bulls, followed by cows with young, then solitary cows. Research into complex and interacting factors such as traffic volume and flow, driver visibility and awareness of moose, and various methods for de-icing roads is further required to determine robust means of mitigating the risk of moose-vehicle collisions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".