LaKind et al. PFAS and Breast Milk
Bibliographic record
Abstract
This file contains data used in: Judy S. LaKind, Marc-Andre Verner, Rachel Rogers, Helen Goeden, Daniel Q. Naiman, Satori A. Marchitti, Geniece M. Lehmann, Erin P. Hines, Suzanne E. Fenton. (2021) Current breast milk PFAS levels in the US and Canada: After all this time why don’t we know more? To estimate nationally representative breast milk concentrations for the US and Canada, we used serum PFOS, PFOA, PFHxS, and PFNA data from the NHANES and the Canadian Health Measures Survey, respectively. To estimate milk concentrations in communities with a known history of PFAS drinking water contamination, geometric mean serum concentrations measured as part of the ATSDR PFAS Exposure Assessments (https://www.atsdr.cdc.gov/pfas/activities/assessments.html) were used. We also used publicly available data from the New York State Department of Health for information on the Hoosick Falls and Petersburgh areas. In Table 1. Serum PFAS, serum or plasma PFAS concentrations (ng/mL) and the number of participants in each cohort (N) are shown. Measured serum/plasma concentrations in national surveys and communities impacted by PFAS were multiplied by milk:serum ratios to estimate breast milk PFAS levels. Figure 1. Breast Milk PFAS shows the results for each dataset.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.183 | 0.072 |
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".