Lessons learned, challenges, and opportunities: The U.S. Endocrine Disruptor Screening Program
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
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.
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 | 0.000 | 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) | 0.000 | 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