Wood Turtle (<i>Glyptemys insculpta</i>) nest protection reduces depredation and increases success, but annual variation influences its effectiveness
Bibliographic record
Abstract
Habitat loss is the leading cause of species extinctions and is especially detrimental to habitat specialists. Freshwater turtles require specific habitat types at different points in their life cycle; notably, the loss of nesting habitat has led to increased nest depredation and adult mortality. In response, conservationists have implemented nest protection and habitat restoration programs to recover declining populations. Although assumed to increase nest survival, effectiveness of these methods has not been rigorously quantified. We located Wood Turtle (Glyptemys insculpta (Le Conte, 1830)) nests in Wisconsin (USA) and conducted two analyses — logistic regression and logistic exposure — to investigate the influence of management actions and environmental factors on nest survival. The depredation rate decreased by 47% for protected nests and declined as nests aged; the success rate increased by 28% for protected nests and increased for nests in areas with fewer roads. We found high annual variation in success, and although weather variables were not predictive, likely due to their coarse scale, we posit that this inter-annual variation was driven by variation in weather conditions. Our results suggest that nest protection is effective at increasing nest survival, but future efforts should span multiple years to account for the effects of annual variation in environmental conditions.
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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".