Introduction to the science and regulation concerning endocrine disrupting chemicals: the challenges ahead
Bibliographic record
Abstract
Presentations in session one of the Society of Environmental Toxicology and Chemistry (SETAC) North America Focused Topic Meeting: Endocrine Disruption (February 4 – 6, 2014) described where the science and the regulations have arrived and identified the key challenges that lie ahead. The first presentation gave an overview of where the endocrine disrupting chemical (EDC) issue currently stands in terms of science and policy. It introduced the significant debate about whether suspected EDCs should be evaluated using a hazard-based or a risk-based approach. Subsequent presentations provided a synopsis of the US-EPA Endocrine Disruption Screening Program (EDSP), including a description of the legislative origins of the program, its risk-based nature, its evolution and its future through the input of multi-stakeholder advisory groups. A presentation was given about the current status of potential regulatory activities in the European Union (EU) relative to EDCs and the fact that it is a highly political subject in Europe was highlighted. Finally an EU- industry perspective was given on the repercussions of hazard versus risk-based approaches for EDCs. Both European speakers noted that the regulatory situation in the EU is not set and that at present it is not possible to predict exactly how EDCs will be addressed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.041 | 0.021 |
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".