Endocrine Disrupting Chemicals Research Program of the U.S. EnvironmentalProtection Agency: Summary of a Peer-Review Report
Bibliographic record
Abstract
At the request of the U.S. Environmental Protection Agency (EPA) Office of Research and Development, a subcommittee of the Board of Scientific Counselors Executive Committee conducted an independent and open peer review of the Endocrine Disrupting Chemicals Research Program (EDC Research Program) of the U.S. EPA. The subcommittee was charged with reviewing the design, relevance, progress, scientific leadership, and resources of the program. The subcommittee found that the long-term goals and science questions in the EDC Program are appropriate and represent an understandable and solid framework for setting research priorities, representing a combination of problem-driven and core research. Long-term goal (LTG) 1, dealing with the underlying science surrounding endocrine disruptors, provides a solid scientific foundation for conducting risk assessments and making risk management decisions. LTG 2, dealing with defining the extent of the impact of endocrine-disrupting chemicals (EDCs), has shown greater progress on ecologic effects of EDCs compared with that on human health effects. LTG 3, which involves support of the Endocrine Disruptor Screening and Testing Program of the U.S. EPA, has two mammalian tests already through a validation program and soon available for use. Despite good progress, we recommend that the U.S. EPA a) strengthen their expertise in wildlife toxicology, b) expedite validation of the Endocrine Disruptors Screening and Testing Advisory Committee tests, c) continue dependable funding for the EDC Research Program, d) take a leadership role in the application of "omics" technologies to address many of the science questions critical for evaluating environmental and human health effects of EDCs, and e) continue to sponsor multidisciplinary intramural research and interagency collaborations.
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.
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 | 0.036 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.049 |
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".