Conditional daily and seasonal movement strategies of male Columbia black-tailed deer (<i>Odocoileus hemionus columbianus</i>)
Bibliographic record
Abstract
Animals move to maximize fitness via resource acquisition, predator avoidance, thermoregulation, or mate access. Variations in movement strategies among and within populations often reflect habitat- or demographic-specific variations in fitness trade-offs. To examine these conditional movement strategies, we modeled seasonal and diel movement patterns of radio-collared adult male Columbia black-tailed deer (Odocoileus hemionus columbianus (Richardson, 1829)) on a temperate, predator-free island. Linear 10 h displacement and home-range areas reached annual maxima during autumn and minima during late winter, corresponding with known dates for breeding season and lowest quality forage, respectively. For all males in all years, initiation of increased movements began during spring and again, abruptly, in late September, immediately prior to peak breeding season. Larger antlered males continued increased movements longer into December, suggesting increased breeding effort relative to smaller antlered males. Time of day predicted movements during all seasons; however, we observed no strong evidence of the crepuscular or nocturnal movement bias typically noted in deer, likely relating to the lack of predators in our study area. In this way, male black-tailed deer adopted conditional, seasonally specific movement strategies to balance fitness trade-offs in resource acquisition, thermoregulation, and mate access.
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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 | 0.000 |
| 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".