Determinants of Legacy Persistent Organic Pollutant Levels in the European Pond Turtle (<i>Emys orbicularis</i>) in the Camargue Wetland, France
Bibliographic record
Abstract
Many banned persistent organic pollutants (POPs) remain for decades in the aquatic environment and can have harmful effects on long-lived predators because of their high bioaccumulation and biomagnification potentials. We investigated the occurrence and levels of 18 polychlorinated biphenyls (PCBs) and 16 organochlorine pesticides in European pond turtles (n = 174) from April to July 2018 in the Camargue wetland, France. Although the Camargue was highly contaminated in previous decades, plasma occurrence and levels of POPs were very low: we were able to quantify only 3 of the 34 compounds we analyzed in >10% of the turtles. The burdens from POPs did not differ between males and females and were uncorrelated with sampling date and body mass. We observed differences in POP burdens between turtles from the 2 sampling sites. One possible explanation is that the sampling sites were in different agricultural hydraulic systems: plasma occurrence and levels were higher for PCB-52 and hexachlorobenzene in turtles captured in drainage channels and for PCB-153 at the site that receives irrigation. Finally, the occurrence and levels of PCB-153 in turtles increased with age, likely because of bioaccumulation and much higher exposure 20 to 30 yr ago than now. Environ Toxicol Chem 2021;40:2261-2268. © 2021 SETAC.
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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".