Using movement behaviour to define biological seasons for woodland caribou
Why this work is in the frame
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Bibliographic record
Abstract
Terrestrial mammals are strongly influenced by seasonal changes in environmental conditions. Studies of animal space use behaviour are therefore inherently seasonal in nature. We propose an individual-based quantitative method for identifying seasonal shifts in caribou movement behaviour and we demonstrate its use in determining the onset of the winter, spring dispersal, and calving seasons. Using pooled data for the population we demonstrate an alternate approach using polynomial regression with mixed effects. We then compare individual onset dates with population-based estimates and those adopted by expert consensus for our study area. Distributions of individual-based onset dates were normally distributed with prominent modes; however, there was considerable variation in individual onset times. Population-based estimates were closer to the peaks of individual estimates than were expert-based estimates, which fell outside the onetailed 90% and 95% sample quantiles of individually-fitted distributions for spring and winter, respectively. Both expertand population-based estimates were later for winter and earlier for both spring and calving than were individual-based estimates. We discuss the potential consequences of neglecting to corroborate conventionally used dates with observed seasonal trends in movement behaviour. In closing, we recommend researchers adopt an individual-based quantitative approach and a variable temporal window for data set extraction.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it