Changes in dental wear and breakage in arctic foxes (<i>Vulpes lagopus</i>) across space and time: evidence for anthropogenic food subsidies?
Bibliographic record
Abstract
Increased human presence in the Arctic may affect its vulnerable ecosystems. Effects on arctic and red foxes provide notable examples. Both have been documented to take anthropogenic subsidies when available, which can change diet and ranging patterns in complex ways that can either benefit or harm populations, depending on the situation. Understanding this complexity requires new tools to study impacts of increasing human presence on endemic mammals at high latitudes. We propose that dental ecology, specifically tooth wear and breakage, can offer important clues. Based on samples of arctic foxes ( Vulpes lagopus (Linnaeus, 1758)) trapped prior to ( n = 78) and following ( n = 57) rapidly growing human presence on the Yamal Peninsula, Russia, we found that foxes trapped recently in proximity to human settlement had significantly less tooth wear and breakage. This is likely explained by a dietary shift from consumption of reindeer ( Rangifer tarandus (Linnaeus, 1758)) carcasses including bone to softer human-derived foods, especially when preferred smaller prey (e.g., West Siberian lemmings, Lemmus sibiricus (Kerr, 1792), and arctic lemmings, Dicrostonyx torquatus (Pallas, 1778)) are unavailable. These results suggest that tooth wear and breakage can be a useful indicator of the consumption of anthropogenic foods by arctic foxes.
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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 | 0.000 | 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".