Rapid Communication: Partitioning of Persistent Lipophilic Compounds, Including Dioxins, Between Human Milk Lipid and Blood Lipid: An Initial Assessment
Bibliographic record
Abstract
A systematic program of sampling and analysis of blood serum for dioxins, furans, and dioxinlike polychlorinated biphenyls (PCBs) has been initiated in the United States through the National Health and Nutrition Examination Survey (NHANES) program. While such data could potentially be used to estimate population-level changes in human milk lipid concentrations of chemicals, such estimates would depend on understanding the relationship between human blood lipid and milk lipid concentrations of the compounds of interest. For dioxins and furans, extremely limited data in humans currently exist for paired blood/milk samples. These data reviewed in this article, support the hypothesis that, over a population and across time, human milk lipid levels of these compounds generally reflect blood lipid levels. However, these data also suggest that significant variations in these ratios are possible among individuals and at various times.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".