Nesting in close quarters: causes and benefits of high density nesting in painted turtles
Bibliographic record
Abstract
Nesting is a costly time for female turtles, both energetically and from threat of predation. Females must ensure maximum survival of offspring for population stability and individual fitness. I observed signs of communal nesting in female Painted Turtles (Chrysemys picta). My goals were to determine; are females choosing to nest at high nest-densities, what cues do they use to select nest sites, are offspring benefitted. Using ArcGIS, I found that females nested in clusters, the location of clusters varied among years, and that nest site selection was not strongly determined by environmental characteristics. When female turtle models were placed on the nesting embankment females nested most often with the highest density of models. In ~25% of cases, nests were so clustered that eggs were deposited directly into existing nests or directly beside existing nests. Survival of clustered nests (49%) was higher than that of solitary nests (39%). In incubators, older clutches had faster incubation times, suggesting embryonic communication as a mechanism promoting hatching synchrony. We strongly suggest that female Painted Turtles choose to nest in close proximity to conspecifics, and that this clustering results in a fitness benefit.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".