Statistical Methods for Identifying Wolf Kill Sites Using Global Positioning System Locations
Bibliographic record
Abstract
Abstract: Accurate estimates of kill rates remain a key limitation to addressing many predator—prey questions. Past approaches for identifying kill sites of large predators, such as wolves ( Canis lupus ), have been limited primarily to areas with abundant winter snowfall and have required intensive ground‐tracking or aerial monitoring. More recently, attempts have been made to identify clusters of locations obtained using Global Positioning System (GPS) collars on predators to identify kill sites. However, because decision rules used in determining clusters have not been consistent across studies, results are not necessarily comparable. We illustrate a space—time clustering approach to statistically define clusters of wolf GPS locations that might be wolf kill sites, and we then use binary and multinomial logistic regression to model the probability of a cluster being a non—kill site, kill site of small‐bodied prey species, or kill site of a large‐bodied prey species. We evaluated our approach using field visits of kills and assessed the accuracy of the models using an independent dataset. The cluster‐scan approach identified 42–100% of wolf‐killed prey, and top logistic regression models correctly classified 100% of kills of large‐bodied prey species, but 40% of small‐bodied prey species were classified as nonkills. Although knowledge of prey distribution and vulnerability may help refine this approach, identifying small‐bodied prey species will likely remain problematic without intensive field efforts. We recommend that our approach be utilized with the understanding that variation in prey body size and handling time by wolves will likely have implications for the success of both the cluster scan and logistic regression components of the technique. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):798–807; 2008)
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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 | 0.030 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".