TOOTH WEAR IN CAPTIVE GIRAFFES (GIRAFFA CAMELOPARDALIS): MESOWEAR ANALYSIS CLASSIFIES FREE-RANGING SPECIMENS AS BROWSERS BUT CAPTIVE ONES AS GRAZERS
Bibliographic record
Abstract
Captive giraffe (Giraffa camelopardalis) mostly do not attain the longevity possible for this species and frequently have problems associated with low energy intake and fat storage mobilization. Abnormal tooth wear has been among the causes suggested as an underlying problem. This study utilizes a tooth wear scoring method ("mesowear") primarily used in paleobiology. This scoring method was applied to museum specimens of free-ranging (n=20) and captive (n=41) giraffes. The scoring system allows for the differentiation between attrition--(typical for browsers, as browse contains little abrasive silica) and abrasion--(typical for grazers, as grass contains abrasive silica) dominated tooth wear. The dental wear pattern of the free-ranging population is dominated by attrition, resembles that previously published for free-ranging giraffe, and clusters within browsing herbivores in comparative analysis. In contrast, the wear pattern of the captive population is dominated by abrasion and clusters among grazing herbivores in comparative analyses. A potential explanation for this difference in tooth wear is likely related to the content of abrasive elements in zoo diets. Silica content (measured as acid insoluble ash) is low in browse and alfalfa. However, grass hay and the majority of pelleted compound feeds contain higher amounts of silica. It can be speculated that the abnormal wear pattern in captivity compromises tooth function in captive giraffe, with deleterious long-term consequences.
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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 | 0.002 | 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".