Deforestation: Risk Of Sex Ratio Distortion In Hawksbill Sea Turtles
Bibliographic record
Abstract
Phenotypic sex in sea turtles is determined by nest incubation temperatures, with warmer temperatures producing females and cooler temperatures producing males. The common finding of highly skewed female-biased hatchling sex ratios in sea turtle populations could have serious repercussions for the long-term survival of these species and prompted us to examine the thermal profile of a relatively pristine hawksbill nesting beach in Guadeloupe, French West Indies. Data loggers placed at nest depth revealed that temperatures in the forested areas were significantly cooler than temperatures in the more open, deforested areas. Using these temperatures as a predictor of sex ratio, we were able to assess the relative contributions of the different beach zones to the primary sex ratio: significantly more males were likely to be produced in the forested areas. Coastal forests are therefore important male-producing areas for the hawksbill sea turtle, and this has urgent conservation implications. On Guadeloupe, as on many Caribbean islands, deforestation rates are high and show few signs of slowing, as there is continual pressure to develop beachfront areas. The destruction of coastal forest could have serious consequences both in terms of local nesting behavior and of regional demography through the effects on population sex ratios. Human alterations to nesting habitat in other reptile taxa have been shown to modify the thermal properties of nest sites in ways that can disrupt their ecology by allowing parasite transmission, increasing vulnerability to climate change, or rendering existing habitat unsuitable.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".