Thermal consequences of subterranean nesting behavior in a prairie-dwelling turtle, the Ornate Box Turtle (<i>Terrapene</i> <i>ornata</i>)
Bibliographic record
Abstract
Many oviparous reptiles deposit eggs in excavated nest chambers, and the location and depth at which eggs are laid can affect predation risk, incubation duration, mortality rates, and hatchling phenotype. Among turtles, nest depth also influences incubation conditions of some large-bodied species, but nest depth is generally expected to vary less among small-bodied species. We monitored nesting behavior of Ornate Box Turtles (Terrapene ornata (Agassiz, 1857)) for two seasons in Illinois. We used direct observations to confirm that, among 31 nesting events, six females oviposited while beneath the substrate surface. Furthermore, comparisons of body length to nest depth indicated that five additional females likely also constructed nests while buried. Nests laid while females were underground were deeper, on average, than other nests (16.7 versus 11.2 cm), and while mean nest temperatures were similar between groups, temperature fluctuations and maximum temperatures were lower among nests that were laid while females were underground. Subterranean oviposition appears to have moderated incubation temperatures by allowing females to deposit eggs at greater depths than would be possible from the surface. This little-documented behavior may be a mechanism for this species to influence the incubation environment, which in turn may influence hatchling phenotypes.
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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".