Diet composition of three amphisbaenian species (<i>Amphisbaena alba, Amphisbaena pretrei</i>, and <i>Amphisbaena vermicularis</i>) from Northeast Brazil
Bibliographic record
Abstract
Amphisbaenians usually have a diet composed of a wide variety of small arthropods, with some species being more selective in their feeding and others considered more generalist. Using only specimens deposited in scientific collections, the diet composition of Amphisbaena alba Linnaeus, 1758, Amphisbaena pretrei Duméril and Bibron, 1839, and Amphisbaena vermicularis Wagler, 1824 from the Northeast region of Brazil was analyzed. Except for individuals of A. alba, due to the small sample size, we also investigated the possible intersexual difference in the volume, length, and number of prey in the diet of amphisbaenians and the possible relationship between prey volume and body size (snout–vent length) of individuals was analyzed. The diet of worm-lizard in general was composed of termites, cockroaches, ants, and beetle larvae, and no intersexual differences were found in the size, length, and number of consumed prey. The body size of A. pretrei and A. vermicularis showed no relationship with the volume of prey consumed. Amphisbaenians presented a characteristic diet of opportunistic generalist predators, with several food categories in the composition of their diets, indicating that the studied species feed according to the availability and abundance of prey in the environment.
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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 | 0.001 |
| 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".