Geographic variation in the probability of being born with and retaining contrasting tail tip colour (tail luring) in the Common Lancehead <i>Bothrops jararaca</i>
Bibliographic record
Abstract
In snakes that are known to be ambush predators, tail luring, in which the movement of a snake’s tail resembles that of a worm or insect larva and is used to attract prey, has emerged as a complementary hunting strategy. In certain species, some individuals may present a conspicuously bright colour at the tail tip, which eventually disappears with age. Some authors argue that the bright colour enhances the resemblance of the snake’s tail with a potential food item, increasing the success of capture. Here, we tested the influence of geographic variation, sex, and environmental factors on the probability that Common Lanceheads Bothrops jararaca (Wied-Neuwied, 1824) from southeastern Brazil were born with this contrasting tail tip and whether snakes retain this trait throughout adulthood. None of the predictors affected the probability of births with a contrasting tail tip. However, a higher proportion of individuals from the coastal populations retained this trait into adulthood. The absence of difference in the probability of being born with this trait indicates that there are other factors influencing tail tip colour, such as phylogenetic correlates, rather than intrinsic or environmental factors. A higher proportion of ectothermic prey in the diet of coastal populations may explain why this population retains tail luring throughout adulthood.
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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 | 0.001 |
| 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".