Seasonal changes in the movement rates and activity patterns of Canada lynx
Bibliographic record
Abstract
In Subarctic and Arctic environments, daily patterns of activity and space-use are strongly influenced by interplay between seasonal abiotic factors and the corresponding responses of the biotic environment. Here, we combined accelerometry with GPS telemetry of Canada lynx ( Lynx canadensis Kerr, 1792, n = 12) in northern Alaska to test the hypotheses that lynx activity would peak during twilight throughout the year, coinciding with activity of their preferred prey, and that individuals with larger home ranges would have greater spatial displacement and expend more energy on movement. Lynx activity occurred throughout the 24 h day and peaked during twilight, but variation among individuals was high and diel rhythms were sometimes only detectable using the finer resolution accelerometer data. Surprisingly, home range size was not correlated with movement costs estimated via acceleration, but step length and acceleration were correlated in a positive curvilinear fashion. However, step length was sometimes disproportionately lower than predicted by acceleration. Such intervals of high activity with low spatial displacement were often followed by periods of rest, suggesting they may be indicative of hunting in a restricted patch of habitat. We conclude that accelerometers can provide additional information to supplement GPS data, providing a more complete picture of animal behavior.
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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".