Mapping global exposure to POPs in human milk through multivariate fingerprinting
Bibliographic record
Abstract
Many persistent organic pollutants (POPs) in human milk present environmental and health concerns, particularly for vulnerable populations such as breastfed infants. This study applied a data exploration dimensionality reduction workflow using Uniform Manifold Approximation and Projection (UMAP) for POP measurements in human milk. This approach focuses on identifying and comparing the compositional patterns-or 'fingerprints'-of POPs rather than just measuring their concentrations. The UMAP approach revealed detailed variations in POP fingerprints that were not detectable with traditional univariate approaches. UMAP approach also improves upon other multivariate approaches, such as hierarchical cluster analysis (HCA) and principal component analysis (PCA). Unlike previous studies focusing solely on concentration differences, UMAP identified distinct regional and economic POP fingerprints. Lower-income countries showed POP fingerprints dominated by DDT-related compounds, while higher-income regions showed distinct fingerprints with greater contributions from PCBs and other legacy pollutants. Temporal analysis captured shifts in POP fingerprints after 2001, corresponding with the expansion from initial focus on PCDD, PCDF, and PCB to the broader group of the original 12 POPs listed under the Stockholm Convention. These results demonstrate how dimensionality reduction techniques, particularly UMAP, can distinguish compositional POP fingerprints across geospatial, temporal, and economic factors, providing a comparative framework for understanding global exposure patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".