MULE DEER SEASONAL MOVEMENTS AND MULTISCALE RESOURCE SELECTION USING GLOBAL POSITIONING SYSTEM RADIOTELEMETRY
Bibliographic record
Abstract
We tracked 12 mule deer (Odocoileus hemionus hemionus) between February 1999 and April 2003 by using global positioning system (GPS) radiotelemetry in southeastern British Columbia to provide detailed information on migration and habitat use to local managers. We tested winter resource selection at the home-range and within-home-range scale to test a hypothesis that ungulate resource selection is scale-dependent. All sampled mule deer in this population migrated from low-elevation winter ranges to high-elevation summer ranges, supporting a hypothesis that migration is obligatory in mountainous, heavy-snow areas. We found little consistent selection at the within-home-range scale, but considerable selection at the home-range scale, supporting a scale-dependent hypothesis. Potential mule deer winter range could be predicted from 2 biophysical attributes, elevation and solar duration. Currently suitable winter habitat can then be further delineated on the basis of amount of mature coniferous forest within this zone. Use of GPS radiotelemetry increased sample intensity of individual deer, and thereby accuracy of individual parameter estimates. However, because of high equipment costs and failure rates, increased sample intensity occurred at the expense of sample size, and therefore illustrates a trade-off consideration for future work.
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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 | 0.000 |
| 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".