Plastic pollution can affect the emergence patterns of loggerhead turtle hatchlings
Bibliographic record
Abstract
Abstract Coastal urbanization, plastic pollution and climate change are increasingly affecting marine turtles' nesting habitats. In addition to facing risks of mortality due to saltwater inundation or predation, their eggs and hatchlings' might also be affected by plastic debris accumulation on beaches, but no studies to date have analysed such impact. To analyse whether plastic pollution on nests' surfaces affects the embryos' and hatchlings' survival odds, we designed a field experiment in a turtle hatchery on a nesting beach of the loggerhead turtle (Caretta caretta) in Boa Vista Island (Cabo Verde). We applied three treatments with distinct plastic levels (18 nests per treatment): control (no added plastics), low density (64 plastic fragments with 24.5 g of plastic weight per nest) and high density (128 plastic fragments with 49.0 g of plastic weight per nest). Then, we tested 16 variables related to the incubation period, emergence period and hatchlings' fitness. Our results suggest that nests with high plastic density have a significantly lower probability of successful emergence. Moreover, plastics also affected the synchronized emergence of hatchlings, with more scattered and smaller emergent groups, which might increase the predation risk. Considering that turtle nesting habitats are becoming increasingly threatened, this additional threat might compromise the survival of turtle hatchlings on beaches.
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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 | 0.000 |
| 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".