Derivation of an aquatic predicted no-effect concentration for the synthetic hormone, 17a-ethinyl estradiol
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 One hundred English language studies published between 1994 and 2007, one as yet unpublished study, and findings published in conference proceedings (in German) were compared to published data quality criteria to identify the most relevant studies for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 (HC<sub>5,50</sub>) of 0.35 ng/L. After comparing this HC<sub>5,50</sub> 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it