Discriminating grey wolf kill sites using GPS clusters
Bibliographic record
Abstract
Abstract Characteristics of spatio‐temporal clusters of locations from global positioning system (GPS)‐collars have been used to distinguish kill sites of various predators. We deployed GPS collars on 9 grey wolves (Canis lupus) in the southwest area of Prince Albert National Park in central Saskatchewan, Canada, and used a GPS location clustering algorithm to identify kill sites of ungulate and other large‐bodied prey during winter, December 2013–March 2017. We used logistic regression in a model‐selection framework to determine if spatio‐temporal and habitat characteristics of grey wolf GPS clusters could be used to reliably identify sites where wolves had killed prey. Global positioning system clusters were more likely to be wolf kill sites when they had a higher number of location fixes, did not begin within 300 m and 30 days of a previous cluster, did not begin within 1 km and 4 days of a previous cluster, began in the evening, had a high percentage of fixes occurring during the day, occurred farther from open habitat, and had both a high number of location fixes and a high percentage of fixes occurring during the day. Our results highlight the limits of using spatio‐temporal clusters with a fix rate of 1/hour to discriminate wolf kill sites in systems dominated by deer (Odocoileus spp.) because of the associated short handling time with these prey.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".