Identifying road avoidance behavior using time‐geography for red deer in Banff National Park, Alberta, Canada
Bibliographic record
Abstract
Abstract Analysis of animal movement as a complex spatiotemporal signal attenuated by behavioural and contextual factors comprises a recent perspective in the time‐geographic study of movement. For their significant ecological and human impacts, animal–roadway interactions have become a particularly important subject matter in this arena. Analyses relying on spatiotemporal aggregation or reductive modelling of the information held in movement trajectories may overlook the influences of behaviour and environmental context. Towards expanding perspectives on animal movement and roadway interactions, this research acknowledges and characterizes the varying influence of temporally dynamic elements in the environmental context at fine spatiotemporal scales. In particular, these elements are hourly traffic volumes and their effect on the probability of animal–roadway interactions. A set of methods from time geography and signal analysis, including the probabilistic space‐time prism, comprehensive probability surface, and cross‐correlation were combined to provide for serial comparison of hourly roadway interaction probabilities and traffic volumes for red deer ( Cervus elaphus ) tracked in Banff National Park, Alberta, Canada. Results suggest a cyclical, diurnal repulsion in roadway interaction probabilities from periods of higher traffic volume at the hourly scale in the study area, consistent with prior theoretical and empirical findings on ungulates living in similar environmental settings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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".