CRITERIA FOR CHEMICAL SELECTION FOR PROGRAMS ON HUMAN MILK SURVEILLANCE AND RESEARCH FOR ENVIRONMENTAL CHEMICALS
Bibliographic record
Abstract
The people of the United States is exposed to a large number of chemicals in their daily lives. In order to prioritize chemicals that should be considered for surveillance of and/or research in human milk, criteria were developed at the Technical Workshop on Human Milk Surveillance and Research on Environmental Chemicals in the United States. The criteria include (1) lipid solubility and/or persistence in the environment; (2) extensive exposure (e.g., high-production-volume chemicals and chemicals in personal care products); (3) known or suspected toxicity in a biological system; (4) historical interest, trend information; (5) chemicals of emerging concern; and (6) chemicals for medicinal use and chemicals in occupational settings. A working list of chemicals was developed for each of the criteria. It should be noted that more than one criterion may be applicable to a selected chemical, but the selected chemical should possess at least one of these designated criteria. It is hoped that by following a cohort of nursing women through their lactational cycle for a group of these chemicals, data generated will indicate the extent of infant exposure and may suggest methods for risk management to decrease inadvertent exposure for breast-feeding mothers and infants. While not the focus of this article, certain endogenous chemicals in human milk beneficial to the health of the infant warrant study as well.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".