Derivation of an aquatic predicted no-effect concentration for the synthetic hormone, 17a-ethinyl estradiol
Bibliographic record
Abstract
17a-Ethinyl estradiol (EE2) is a synthetic estrogen widely used in combination with othersteroid hormones in oral contraceptives and in the contraceptive patch. EE2 has been detected in sewage treatment plant effluents in the low nanogram -per-liter range and occasionally in surface waters in the U.S., U.K., Canada, Brazil, Germany, and elsewhere. The mode of action is receptor-mediated, and estrogen receptors exist in mammals and other vertebrates. A large number of studies on the effects of EE2 on aquatic organisms exist. One hundred English language studies published between 1994 and 2007, one as yet unpublished study, and findings published in conference proceedings (in German) were compared to published data quality criteria to identify the most relevant studies for deriving a predicted no-effect concentration (PNEC). Reproduction in fish was identified as the most sensitive end point in aquatic species. A species sensitivity distribution was constructed using no observed effect concentrations (NOECs) for reproductive effects from 39 papers in 26 species, resulting in a median hazardous concentration at which 5% of the species tested are affected (HC5,50) of 0.35 ng/L. After comparing this HC5,50 to all of the laboratory and field-derived toxicity information available for EE2, we recommend using 0.35 ng/L as the PNEC for EE2 in surface water. This PNEC is below 95% of the existing NOECs for effects on reproduction and is also below virtually all of the NOECs for vitellogenin induction in the key fish reproduction studies.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".