Interactive effects of sex and body size on the movement ecology of adfluvial bull trout (<i>Salvelinus</i> <i>confluentus</i>)
Bibliographic record
Abstract
Animal movement occurs as a function of many factors including changing environmental conditions (e.g., seasonality) and the internal state (e.g., phenotypic traits) of the focal organism. Identifying how these factors interact can reveal behavioral patterns that would otherwise go undiscovered. Given a large sample size of individuals (n = 187), we used acoustic biotelemetry to examine the spatial ecology of adfluvial bull trout (Salvelinus confluentus (Suckley, 1859)) in a large hydropower reservoir in British Columbia, Canada. Dependent variables, including home-range size and lateral movement, were analysed as a function of interactive relationships among seasons (over a 2-year period) and phenotypic traits. Mixed models indicated relationships between home-range size and season, whereas variation in lateral movement was explained by month and a two-way interaction between sex and body size. Large females (765 mm total length) were estimated to move laterally up to five times greater than females half their length, whereas movements between large and small males were not significantly different. This study shows how body size and sex can have a profound and possible interactive effect on animal movement. In addition, the results offer new information on the spatial ecology and conservation of adfluvial bull trout.
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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 | 0.000 | 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".