Chemosensory discrimination of plant and animal foods by the omnivorous iguanian lizard <i>Pogona vitticeps</i>
Bibliographic record
Abstract
Most iguanian lizards are insectivores that do not use chemical cues sampled by tongue-flicking to identify prey before attacking, but the sole iguanian herbivore previously studied did so. To investigate the effects of a partially herbivorous diet on responses to food chemicals, I conducted an experiment to determine whether the omnivorous bearded dragon (Pogona vitticeps) has a similar ability. Chemical stimuli from crickets and carrots, both preferred foods, and alfalfa sprouts, and deionized water (a nonpreferred food and odorless control, respectively) were presented on cotton-tipped applicators. The lizards responded more strongly to both preferred foods than to the controls, performing more tongue flicks and biting the cotton in a greater number of trials. It is hypothesized that lingually mediated food-chemical discrimination is useful to herbivorous and omnivorous lizards for identifying plant and animal foods and for evaluating the quality of plant foods. The insectivorous ambush foragers ancestral to P. vitticeps could not locate prey by tongue-flicking repeatedly at an ambush post and do not exhibit prey-chemical discrimination. Adding plants to the diet altered the selective milieu because plants approached using visual cues can be evaluated using chemical cues, allowing the evolution of the ability to discriminate between plant-food chemicals. The ability to identify animal prey by tongue-flicking may have evolved through correlated evolution with chemosensory identification of plants or specifically for locating or identifying immobile prey.
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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 | 0.000 | 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".