Bibliographic record
Abstract
ABSTRACT Traditionally, seasons for animals have been designated based on single external variables such as climate or plant phenology, rather than an animal's response to the dynamic environments within which it lives. By interpreting a rate of movement function of cumulative movement through time we established a method that distinguishes transitions between behaviors limited by winter habitat conditions from those present during summer. Identification of these time periods provides temporal definition to subsequent home‐range analyses and use‐availability comparisons. We used location data from 32 Global Positioning System‐collared female moose ( Alces alces ) to demonstrate the method. We used model selection (Akaike's Information Criterion) to differentiate between candidate rate of movement response curves. Of 32 moose, 29 clearly conformed to an annual movement pattern described by a logistic curve, with increased rates of movement in summer compared to winter. Conversely, 3 aberrant individuals did not alter their movement rate through the year and were best fit with a linear response curve. The seasonal rate of movement model we developed suggests an average summer period of 122 days (median = 119 days, range = 96–173 days) for moose in northwestern Ontario, Canada. The rate of movement model we applied to individuals indicated 1 May as the median date for the winter‐summer transition (range = 2 Apr–24 May), and the median transition from summer to winter was 25 August (range = 1 Aug–23 Oct). Wide variation in timing and duration of summer and winter seasons among individuals demonstrates potential failure of the single external variable approach to capture the suite of factors potentially influencing animal behaviors. By plotting cumulative distance moved throughout the year, we elucidated individual variation in response to known and unknown variables that affect animal movement. Accounting for variability among individuals in designation of biologically significant temporal boundaries is critical to delineation of seasonally important habitats for conservation and sustainability of healthy wildlife populations.
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How this classification was reachedexpand
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 | 0.001 | 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 teacher head, 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".