Multi-scale foraging decisions made by woodland caribou (<i>Rangifer tarandus caribou</i>) in summer
Bibliographic record
Abstract
Multi-scale selection patterns can be understood from two perspectives: coarse-scale patterns as the summation of fine-scale patterns (scaling-up), or as a hierarchy produced from multiple contributory factors with differential effects on organismal fitness (hierarchical). We examined woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)) selection of foraging locations across two spatiotemporal scales to test whether selection patterns between them were consistent (scaling-up) or different (hierarchical) to determine which framework most accurately describes their foraging behaviour. Seven adult female woodland caribou were equipped with GPS telemetry radio collars outfitted with high-definition video cameras that recorded woodland caribou foraging choices throughout the summer. Fine-scale data from videos combined with direct measurements in the field along movement trajectories obtained from GPS fixes were used to estimate (i) feeding station selection and (ii) food patch selection. We estimated resource selection functions for each scale following a use–availability structure. Woodland caribou exhibited resource selection at both scales. Apart from selection for species of the lichen Cladina (Nyl.) Nyl. and patches associated with high abundance of Cladina, few patterns were consistent across both scales. Our study suggests that even at very fine scales, woodland caribou selection for foraging locations is hierarchical in nature.
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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) | 0.000 | 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".