Use of Camera Traps Provides Insight into the Feeding Ecology of Red Foxes (<i>Vulpes vulpes</i>)
Bibliographic record
Abstract
Red Foxes (Vulpes vulpes) often carry food items to caching sites and while making provisioning trips to litters. This behaviour provides opportunities to use camera traps to record Red Foxes carrying food that is likely prey. As part of a larger study using camera-trap surveys to monitor carnivore populations at Great Swamp National Wildlife Refuge, New Jersey, our cameras also recorded Red Foxes carrying food items allowing us to gain insight into the feeding ecology of this predator. Camera traps documented Red Foxes carrying food 71 times; items included mammals (78.9%), birds (19.7%), and fish (1.4%). Small mammals (unknown rodent or soricid species [23.9%] and voles [Microtus or Clethrionomys spp.; 5.6%]) were the most common groups of food items and Muskrat (Ondatra zibethicus; 15.5%) was the most common food item identified to species. Our surveys corroborate traditional diet assessments (e.g., scat analysis) of Red Foxes in North America, identifying them as a generalist forager that typically consumes smaller mammals. We also highlight the potential to apply camera trapping as a supplemental technique for gaining additional insight into the feeding ecology of this predator.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".