When generalists behave as specialists: local specialization by American badgers (<i>Taxidea</i> <i>taxus</i>)
Bibliographic record
Abstract
Differentiating species according to their relative niche breadth is a common approach in community ecology that can enhance understanding about how species relate to the environment. Although a species might exhibit a generalized ecology across its entire range, on a local scale, individuals might function as specialists. American badgers (Taxidea taxus (Schreber, 1777)) are terrestrial carnivores that have been described most often as generalists. We compared patterns of habitat selection by badgers at three scales to test the hypothesis that badgers would exhibit behaviors more closely aligned with specialists than generalists when inhabiting landscapes with black-tailed prairie dogs (Cynomys ludovicianus (Ord, 1815)). At a course scale, badgers selected for prairie dog colonies across the landscape. At an intermediate scale, we documented significantly greater use of prairie dog colonies within the home ranges of badgers. At fine scales within colonies, badgers used areas that had relatively high densities of prairie dog burrows, where prey was presumably abundant. On multiple scales, badgers exhibited a narrow use of resources in comparison with the resources available. Our study provides additional evidence that badgers behave as specialists when burrowing rodents are highly concentrated and predictable over space and time.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".