Endocrine disruption: where have we been, interpretation of data, and lessons learned from Tier 1
Bibliographic record
Abstract
In response to the requirements of the US EPA’s Endocrine Disruptor Screening Program, Tier 1 assays have been performed with a number of pesticides over the past several years. These assays are designed to be used in concert as a screen for potential interactions with vertebrate estrogen, androgen, and thyroid systems. The results of the 11 assays in the Tier 1 battery are then used, along with other lines of evidence, to determine whether a chemical is endocrine-active and, as a consequence, might be a candidate for Tier 2 testing. An overview of the Tier-1 testing program was presented in Session Two of the Society of Environmental Toxicology and Chemistry (SETAC) North America Focused Topic Meeting: Endocrine Disruption Chemical Testing: Risk Assessment Approaches and Implications (February 4 – 6, 2014). Subsequent presentations discussed the concept of weight-of-evidence (WoE) and assessment of Tier 1 results in a WoE framework. The importance of scientifically credible, transparent approaches for conducting WoE analyses was recognized, and approaches for framing the hypotheses, evaluating the data, assigning weight to different endpoints relative to their diagnostic effectiveness, and assessing confounding factors were presented. In recognition of the cross-species conservation of the hypothalamic-pituitary-gonadal axis among vertebrates, a subset of the Tier-1 in vivo assays may be useful for more rapidly screening chemicals for potential endocrine activity.
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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 | 0.132 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.025 | 0.027 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".