Linear features impact predator-prey encounters: analysis with first passage time
Bibliographic record
Abstract
Landscapes are heterogeneous and animals respond to this heterogeneity by altering their movement patterns. This thesis was motivated by the need to understand the impact of a particular type of heterogeneity, anthropogenic linear features, on wolf (Canis lupus) movement in the central east slopes of the Rocky Mountains (Alberta, Canada). First passage time refers to the length of time taken to first encounter an object, such as a prey item. Novel first passage time analysis methods for animal movement were developed and applied to wolves in the presence of linear features. The underlying movement model was parameterized using paths obtained from GPS collars. These animal movement paths were confounded by measurement error. I developed a mechanistic, empirically-based method for buffering linear features that minimized the underestimation of animal use of linear features introduced by GPS measurement error. Mean first passage time analysis showed that wolves found prey faster in landscapes with higher densities of linear features, resulting in an increased functional response, which was most prominent at low prey densities. These findings have implications for management of species at risk in highly developed landscapes.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".