Spring emergence of Eastern Box Turtles (<i>Terrapene carolina</i>): influences of individual variation and scale of temperature correlates
Bibliographic record
Abstract
Many organisms spend considerable time in dormancy to avoid stressful environmental conditions. Understanding the timing and triggers of dormancy behavior is critical for understanding an animal’s life history and behavior. Eastern Box Turtles (Terrapene carolina (L., 1758)) avoid winter temperatures by burrowing into the soil and remaining dormant. Identifying the proximate environmental cues that trigger emergence can improve conservation efforts by reducing potential aboveground turtle mortality. During a 17-year study, half of all variation in emergence timing was attributed to individual variation and the habitat that they occupied during dormancy. We suggest that individual variation in emergence timing is common within populations and confounds efforts to identify reliable emergence cues. Additionally, the scale of meteorological data limits the ability to identify emergence predictors. Using data from temperature loggers placed at dormancy locations, we found that surface air temperatures, averaged over the 5 days prior to emergence, were more strongly related to emergence probability than any variables derived from local weather stations. Turtles generally did not emerge from dormancy until the 5-day mean surface temperatures measured at dormancy sites reached approximately 15 °C. Our results suggest that individuals respond differently to environmental thresholds for emergence and individuals may be characterized as risk-taking or risk-aversive.
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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 | 0.000 | 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".