Dirty Turtles: Examining the Effects of Persistent Pollutants on Embryonic Turtle Development
Bibliographic record
Abstract
Background: In an anthropogenically-altered world, it is common to find pollutants such as plastics, pesticides, fertilizers, and heavy metals in waterways and soil. Many plastics, such as BPAs; organic chemicals that can absorb plastics, such as PCBs; and pesticides, such as atrazine, are known to be endocrine disrupting chemicals (EDCs). Many EDCs act as xenoestrogens and can affect the sexual development of numerous organisms, from mammals, such as humans; to reptiles, such as turtles. Turtles are long-living organisms that often have an omnivorous diet and demonstrate site fidelity, thus they are useful subjects in which to study the effect of EDCs on organismal development. Methods: In this study, the effects of plastics, pesticides, flame retardants, and heavy metals on developing turtle embryos were examined across a variety of both freshwater and marine turtles. A search of existing relevant literature was carried out in November of 2016 using the database Web Of Science and Google Scholar by looking for the keywords “turtles” AND “sex-reversal” AND “endocrine disrupting chemicals (EDCs)”, with no restrictions used on the years in which these studies were published. Web of Science returned 42 articles and Google scholar returned 3870 articles. Of these, 13 studies were deemed relevant and examined, encompassing 35 cases, and consisting of data from eight different species of turtles. Summary: It was found that plastics and PCBs, even in low doses, had the greatest effects on sex reversal across turtle species, and could affect their behavior later in life as well. Pesticides showed an ability to alter the sex of the turtle, and also caused developmental defects. Flame-retardants and heavy metals were shown to be maternally transferred to offspring, but studies did not find obvious cases of sex reversal or developmental defects at low doses. Many turtle species are endangered, and thus understanding threats to their health and development is critical to their future survival.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".