Lessons learned, challenges, and opportunities: The U.S. Endocrine Disruptor Screening Program
Bibliographic record
Abstract
In 1996, the U.S. Congress passed the Food Quality Protection Act and amended the Safe Drinking Water Act (SDWA) requiring the U.S. Environmental Protection Agency (EPA) to implement a screening program to investigate the potential of pesticide chemicals and drinking water contaminants to adversely affect endocrine pathways. Consequently, the EPA launched the Endocrine Disruptor Screening Program (EDSP) to develop and validate estrogen, androgen, and thyroid (EAT) pathway screening assays and to produce standardized and harmonized test guidelines for regulatory application. In 2009, the EPA issued the first set of test orders for EDSP screening and a total of 50 pesticide actives and 2 inert ingredients have been evaluated using the battery of EDSP Tier 1 screening assays (i.e., five in vitro assays and six in vivo assays). To provide a framework for retrospective analysis of the data generated and to collect the insight of multiple stakeholders involved in the testing, more than 240 scientists from government, industry, academia, and non-profit organizations recently participated in a workshop titled "Lessons Learned, Challenges, and Opportunities: The U.S. Endocrine Disruptor Screening Program." The workshop focused on the science and experience to date and was organized into three focal sessions: (a) Performance of the EDSP Tier 1 Screening Assays for Estrogen, Androgen, and Thyroid Pathways; (b) Practical Applications of Tier 1 Data; and (c) Indications and Opportunities for Future Endocrine Testing. A number of key learnings and recommendations related to future EDSP evaluations emanated from the collective sessions.
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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 | 0.033 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".