Assessing and Managing Risks Arising from Exposure to Endocrine-Active Chemicals
Bibliographic record
Abstract
Managing risks to human health and the environment produced by endocrine-active chemicals (EAC) is dependent on sound principles of risk assessment and risk management, which need to be adapted to address the uncertainties in the state of the science of EAC. Quantifying EAC hazard identification, mechanisms of action, and dose-response curves is complicated by a range of chemical structure/toxicology classes, receptors and receptor subtypes, and nonlinear dose-response curves with low-dose effects. Advances in risk science including toxicogenomics and quantitative structure–activity relationships (QSAR) along with a return to the biological process of hormesis are proposed to complement existing risk assessment strategies, including that of the Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC 1998 EDSTAC. 1998. Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC) final report, Washington, DC: U.S. Environmental Protection Agency. [Google Scholar]). EAC represents a policy issue that has captured the public's fears and concerns about environmental health. This overview describes the process of EAC risk assessment and risk management in the context of traditional risk management frameworks, with emphasis on the National Research Council Framework (1983) National Research Council. 1983. “Committee on the Institutional Means for Assessment of Risks to Public Health. Commission on Life Sciences”. In Risk assessment in the federal government: Managing the process, Washington, DC: National Academy Press. [Google Scholar], taking into consideration the strategies for EAC management in Canada, the United States, and the European Union.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".