Effects of phenology and sex on social proximity in a gregarious ungulate
Bibliographic record
Abstract
Structure in sociality is known to relate to intrinsic and extrinsic factors. Less understood are the mechanics of sociality expressed as fine-scale behaviours that maintain hierarchies, mediate competition, or transmit pathogens. A recent novel approach to quantifying fine-scale social behaviour has been to use proximity-logging biotelemetry collars. This technology continuously records data whenever collars are within a predefined distance of each other, at times of day, and in habitats where traditional ethological approaches to focal-individual sampling of behaviours are unfeasible. We tested a series of expectations on fine-scale (≤1.4 m) interaction rates and durations consistent with competing hypotheses of seasonal and sexual segregation for elk (Cervus canadensis Erxleben, 1777). Female–female dyads interacted 4 times more frequently than male–male dyads (mean interaction rate per year: female–female = 62 vs. male–male = 14; P < 0.001), and male–male interactions were 1.5 times longer in duration than female–female interactions (mean interaction length: female–female = 30 s vs. male–male = 45 s; P < 0.001). We propose that fine-scale interactions among members of a population can be modeled as a trade-off between the frequency (quantity) and the duration (quality) of interactions. Our results have implications for understanding sex-based differences in sociality in gregarious herbivores and for disease transmission, which may follow from social intercourse.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".