Factors influencing the emergence of a northern population of Eastern Ribbon Snakes (Thamnophis sauritus) from artificial hibernacula
Bibliographic record
Abstract
We investigated whether Eastern Ribbon Snakes ( Thamnophis sauritus (L., 1766)) use a rise in water level as a cue for emergence from hibernation. We also examined the hypotheses that snakes use temperature gradients or endogenous signals as emergence cues. Twelve artificial hibernacula were used to house 15 Ribbon Snakes. Water level and temperature were regulated. Four Ribbon Snakes emerged from hibernation without any manipulation of water level or temperature. Eight snakes emerged after thermal conditions in their hibernacula changed. Of these, one emerged after the hibernaculum was made warmer on the surface than at depth, four emerged after the room temperature was increased to 9 °C, and three emerged after incandescent lights were shone on the surface of each hibernaculum. Three snakes died during hibernation. Eight snakes chose to hibernate fully submerged in water. Although the sample size is too small to draw conclusions that are statistically significant at α = 0.05, our observations collectively suggest that Ribbon Snakes do not use a rise in water level as a cue to emerge. While water-level rise does not appear to be an emergence cue, hibernation below the water table may lead to increased survivorship by decreased metabolism and elimination of the risk of desiccation.
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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".