Using Accelerometer Data to Remotely Assess Predation Activity of Arctic Wolves <i>(Canis lupus arctos)</i>
Bibliographic record
Abstract
Arctic wolves (Canis lupus arctos) play an important role in ecosystems located in the far northern regions of the world; however, little information is available about them and their impacts on prey populations due to their remote location. Recently, there has been concern about declining caribou populations, which serve as an important food source for local Inuit peoples. As a result, there is an urgent need to better understand Arctic wolves and their influence on caribou abundance. Dr. Dan MacNulty, a professor at Utah State University, is currently conducting research on Arctic wolves in the Fosheim Peninsula of Ellesmere Island, Canada. In July 2014, four Arctic wolves, each from a different pack in the area, were captured and temporarily fitted with a global positioning system (GPS) radio-collar equipped with an accelerometer that records activity levels. Because capturing and eating prey takes time, clusters of GPS locations can be used to identify wolf predation events. The objective of my project is to evaluate the utility of collar accelerometer data for inferring the presence of wolf-killed ungulates at GPS location clusters. Because predation is an energetically-intensive activity, I expected location clusters with high levels of activity at the onset of cluster formation to contain kills. Collar accelerometer data may provide a new tool for scientists and wildlife managers to remotely monitor the predatory impact of large carnivores.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".