Malocclusion in an Arctic Wolf (<i>Canis lupus arctos</i>) from northeast Greenland
Bibliographic record
Abstract
I document the first case of malocclusion in an Arctic Wolf (Canis lupus arctos) from Greenland. All canine teeth of a wolf found dead on the tundra of northeast Greenland showed evidence of heavy anterior wear resulting from occlusion with the opposite teeth. Additional heavy wear on the incisors indicated a level bite. No cases of malocclusion were found in the largest museum collection of Arctic Wolf skulls (n = 11) from Greenland. However, the collection consisted exclusively of specimens from a northeast Greenland wolf population extirpated ca. 1939; thus, it provided no information on the incidence of malocclusion in more contemporary wolves. A finding of malocclusion in the more recent wolf population could be important because the condition is genetically based and the trait is expressed more frequently with increased inbreeding. The small, geographically isolated wolf population that this wolf was a part of disappeared for reasons unknown after 2002 and genetic conditions cannot be excluded as a contributing factor. Future study of the prevalence and severity of this abnormality in Arctic Wolves from Greenland will be problematic because of the difficulty of acquiring comparative material, but could be conducted on other populations of Arctic Wolves.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".