Scoring Body Condition in Wild Baird’s Tapir ( <i>Tapirus bairdii</i> ) Using Camera Traps and Opportunistic Photographic Material
Bibliographic record
Abstract
Body condition score (BCS) systems have been used in wild animals as a technique for evaluating the health status of species that are difficult to capture but can be observed in their habitat. In this study, our goal was to enable scoring the BC of wild Baird’s tapir ( Tapirus bairdii) without the need for direct observation, using camera trap and opportunistic photographic records. First, we modified a BCS assessment that was created for other tapir species, using captive Baird’s tapirs. Second, we applied it to a set of photographs of wild Baird’s tapir that were obtained over six consecutive years in a protected area in southern Mexico. We compared morphometric measurements and muscle and fat deposited in several anatomical regions. We also evaluated changes in BC between seasons for individuals photographed on several occasions. We show that neck and thorax circumferences are significantly correlated with all BCSs associated with these anatomical regions, whereas abdominal circumference is correlated only with half of the BCS. BCS of captive tapirs that we evaluated averaged 24.93 ± 5.61, which was higher than that of wild tapirs (22.63 ± 3.68). No significant difference in BC was apparent between rainy and dry seasons in our study site; wild tapirs were able to maintain good BC throughout the year. Camera trap records and opportunistic photographs were a useful tool to track changes in BC over 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 | 0.000 |
| 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 | 0.000 |
| 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".