Vulpes vulpes and Vulpes lagopus morphometric data_Canadian low Arctic
Bibliographic record
Abstract
This data set contains morphometry data and individual characteristics of red foxes (Vulpes vulpes) and Arctic (Vulpes lagopus) legally harvested in 2017 and 2018 in and around Churchill, Manitoba, Canada. This area is located near treeline, on the western coast of the Hudson Bay, and its winters are characterized by harsh abiotic conditions and prey scarcity. A red fox population became established on the coastal tundra, possibly competing with the native Arctic fox. We used these data to quantify species-size difference to predict the potential strength of interference competition, notably the likelihood of the larger competitor escalating interference to intraguild killing. Size difference was intermediate ( see Donadio & Buskirk, 2006) in body mass and linear dimension, which would favor strong interference competition over limited resources, with a high risk of red foxes escalating interference to intraguild killing. Animals were aged using canine cementum annuli count (Matson’s lab, Manhattan, Montana, USA). Note that we only sent canines with a proportional size of pulp cavity compared to tooth width of 40% and less, a threshold under which we considered animals were subadults.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.017 |
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".