Omnivorous lacertid lizards (<i>Gallotia</i>) from El Hierro, Canary Islands, can identify prey and plant food using only chemical cues
Bibliographic record
Abstract
We studied lingual and biting responses to food chemicals by two species of omnivorous lacertid lizards, the Canary Island endemics Gallotia simonyi (the giant lizard of El Hierro) and Gallotia caesaris (Boettger's lizard), to ascertain their ability to discriminate between prey and plant food chemicals on the one hand and control stimuli on the other. We recorded frequencies of tongue-flicking and latency to bite in 60-s trials in which chemical stimuli on cotton-tipped applicators were presented to the lizards. Both species exhibited prey-chemical discrimination, as indicated by elevated tongue-flick rates and higher proportions of individuals biting in response to surface chemicals from crickets. Both species exhibited plant-chemical discrimination, as indicated by significantly greater tongue-flick rates and biting frequency in response to chemicals from tomato fruit than to the control stimuli. Juvenile G. simonyi responded much more strongly to chemical stimuli from tomato fruit than from leaves of Psoralea bituminosa, which is not a preferred food for juveniles. The findings are consistent with the hypothesis that chemosensory discrimination evolves in omnivorous lizards to permit evaluation of food quality, resulting in correspondence between plant diet and plant-chemical discrimination, both being absent in insectivores. The results are also consistent with the hypothesis that prey-chemical discrimination is retained and plant-chemical discrimination evolves in the omnivorous lizards derived from actively foraging insectivores.
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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".