Do GPS clusters really work? carnivore diet from scat analysis and GPS telemetry methods
Bibliographic record
Abstract
Abstract Global Positioning System (GPS) data collected using radiocollars have allowed researchers to identify sites where predators have killed prey, but this method has yet to be compared with scat analysis, a more traditional method of determining diet composition. We analyzed 211 scat samples and compared composition of prey items with 266 kill sites found using GPS radiotelemetry data on cougars ( Puma concolor ) in the Cypress Hills of southeast Alberta and southwest Saskatchewan, Canada. Scat and kill site results showed significantly different occurrences of prey items; scat samples were better able to detect small mammals. However, larger prey made up >90% of the biomass of cougar diets, and when restricting the comparison to ungulate prey, both methods estimated nearly identical biomass consumed. As expected, GPS telemetry is biased against small prey but the method provides results comparable to scat analysis for larger prey that make up the majority of biomass consumed. © 2011 The Wildlife Society.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".