Crypsis influences escape decisions in the Round-tailed Horned Lizard (Phrynosoma modestum)
Bibliographic record
Abstract
Flight initiation distance (predator–prey distance when escape begins) increases as predation risk increases. Prey should have longer flight initiation distance when their background, movement, or current posture reduces crypsis. Flight initiation distance of ectotherms may increase at lower body temperature to compensate for slower running speed. However, for cryptic prey, fleeing might increase the probability of being detected. The Round-tailed Horned Lizard ( Phrynosoma modestum Girard, 1852) is eucryptic and resembles small stones. We predicted that flight initiation distance by P. modestum is shorter among stones than on uniform sand. Because movement and upright posture disrupt crypsis, we predicted that flight initiation distance is greater after movement and when standing than when still and lying on the ground. As predicted, flight initiation distances were shorter on a rocky than sandy area, when lying flat than standing, and while immobile than after moving. We measured running speed and flight initiation distance to determine relationships among body temperature, speed, and escape decisions. Running speed and flight initiation distance were reduced at lower body temperature, suggesting that crypsis reinforced by immobility is more advantageous than longer flight initiation distance for cool, slow lizards. The lizards adjusted escape decisions to current effectiveness of crypsis and escape ability.
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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".