Year-round patterns of mineral lick use by Moose (<i>Alces americanus</i>), deer, and Elk (<i>Cervus canadensis</i>) in north-central British Columbia
Bibliographic record
Abstract
Natural mineral licks are important to the physiological ecology of several species of ungulates in North America and abroad. Information on year-round patterns of mineral lick use by ungulates in Canada is poorly understood. We used camera traps to record patterns of mineral lick use by four ungulate species visiting five naturally occurring mineral licks located within the John Prince Research Forest and surrounding area, near Fort St. James, British Columbia, Canada. Our cameras detected over 1800 mineral lick visits by ungulates from February 2017 to January 2018. Mineral licks were visited year-round, however, most visits were made between May and September during morning hours. We observed variable lick visitations among sites, species, and sex and age classes. The species observed in descending number of lick visits included Moose (Alces americanus), White-tailed Deer (Odocoileus virginianus), Elk (Cervus canadensis), and Mule Deer (Odocoileus hemionus). Some licks were visited by all four species, while others were visited by fewer. Female ungulates were recorded at licks more frequently than males or juveniles, which likely reflected the underlying sex and age structure of the population. Elk spent more time at licks than Moose and deer and there was no difference in visit durations between Moose and deer. Most visits were made by single animals, but group visits were also observed. Our findings provide evidence that mineral licks are used year-round by ungulates and appear to be important habitat features on the landscape.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".