The effectiveness of decommissioning roadside mineral licks on reducing moose (<i>Alces alces</i>) activity near highways: implications for moose–vehicle collisions
Bibliographic record
Abstract
Roadside mineral licks form when road salt used to de-ice highways in winter runs off road surfaces and accumulates in roadside ditches. Some ungulates are attracted to these roadside licks as they seek to satisfy their mineral requirements. Within the distribution of moose (Alces alces (Linnaeus, 1758)) in North America, motorists often encounter moose visiting roadside licks in mid-summer, with many jurisdictions reporting summer peaks in moose–vehicle collisions (MVCs) at these locations. We used camera traps to monitor the moose visitation of 22 roadside locations (including roadside licks, roadside ponds, and dry roadsides) in central British Columbia, Canada, from December 2009 to July 2020. We tested the efficacy of treatment (decommissioning) methods used to reduce moose visitation to roadside licks and roughly estimated decommissioning costs. Moose visitation to roadside licks was greatest from May to July. As we hypothesized, untreated licks were visited more often by moose than decommissioned licks, roadside ponds (absence of road salt), and dry roadsides. Decommissioning roadside licks by replacing or mixing lick waters and soils with materials, such as riprap, cedar mulch, pine logs, or dog (Canis lupus familiaris Linnaeus, 1758) fur and human (Homo sapiens Linnaeus, 1758) hair, is an effective and inexpensive means of reducing moose visitations to roadside areas and should increase motorist safety where roadside licks are visited by moose.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".