The effect of captivity on the oral health of the critically endangered black-footed ferret (<i>Mustela</i> <i>nigripes</i>)
Bibliographic record
Abstract
Black-footed ferrets (Mustela nigripes (Audubon and Bachman, 1851)), a North American species of mustelid, faced near extinction after westward expansion during the 20th century destroyed a majority of the population of prairie dogs (genus Cynomys Rafinesque, 1817), their primary food source. Fearing extinction of the black-footed ferret, the U.S. Fish and Wildlife Service captured the entire population between 1985 and 1987 and began a captive breeding program. While in captivity, the fertility and genetic diversity of the species was closely monitored; however, there is little information about other health consequences of this breeding program. For instance, the black-footed ferrets have been fed a diet that is very different than what they consume in the wild. How did the composition of this diet affect the oral health of these animals? An analysis of dentition of wild and captive black-footed ferrets reveals that calculus accumulation and periodontal diseases occurred with greater severity in captive black-footed ferrets, suggesting that such oral pathologies arose from the unnaturally soft diet fed to them. These findings offer insight into how mechanical properties of diet can affect oral health and how these dietary properties should be considered, not only in regard to the health of black-footed ferrets but also to the health of all mammals including humans.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".