CONSIDERATIONS FOR NATURAL MINERAL LICKS USED BY MOOSE IN LAND USE PLANNING AND DEVELOPMENT
Bibliographic record
Abstract
Despite an increasing body of knowledge about the predictable use and functional role of naturally occurring mineral licks in the ecology of ungulates such as moose (Alces alces), no documents have been published that discuss the importance of implementing management guidelines aimed to protect these habitat features. We reviewed the literature on the biophysical attributes of mineral lick sites and their use by moose to illustrate the importance of licks and outline criteria that may serve to help in the development of guidelines to protect these land features. We canvassed the provinces and territories of Canada to ascertain whether any regulatory framework for identifying, classifying, and protecting mineral licks existed. Despite appeals for lick protection from several authors, few jurisdictions recognize mineral licks as a special habitat feature and none appear to base their guidelines for protecting licks on ecological principles. We also found no evidence for the existence of a set of standardized guidelines that can be used by planners and managers to ensure the protection of licks. We incorporated ecological and biophysical aspects of mineral licks into a field checklist to identify and classify mineral licks used by moose, and developed a preliminary draft of a management procedure to enable their protection. ALCES VOL. 40: 161-167 (2004)
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".