Characterizing Important Dietary Exposure Sources of Perfluoroalkyl Acids in Inuit Youth and Adults in Nunavik Using a Feature Selection Tool
Bibliographic record
Abstract
BACKGROUND: Previous studies have identified the consumption of country foods (hunted/harvested foods from the land) as the primary exposure source of perfluoroalkyl acids (PFAA) in Arctic communities. However, identifying the specific foods associated with PFAA exposures is complicated due to correlation between country foods that are commonly consumed together. METHODS: ). Adaptive elastic net, a machine learning technique, identified the most important food items for predicting PFAA biomarker levels while accounting for the correlation among the food items. We used generalized linear regression models to quantify the association between the most predictive food items and six plasma PFAA biomarker levels. The estimates were converted to percent changes in a specific PFAA biomarker level per standard deviation increase in the consumption of a food item. Models were also stratified by food type (market or country foods). RESULTS: was also associated with higher concentrations of perfluorohexanesulphonic acid (PFHxS) and perfluorononanoic acid (PFNA), albeit with lower percentage changes. PFHxS, perfluorooctanoic acid (PFOA), and PFNA followed some similar patterns, with higher levels associated with frequent consumption of ptarmigan [6.1% (95% CI: 3.2%, 9.0%); 5.1% (95% CI: 1.1%, 9.1%); 5.4% (95% CI: 1.8%, 9.0%)]. Among market foods, frequent consumption of processed meat and popcorn was consistently associated with lower PFAA exposure. CONCLUSIONS: , and ptarmigan. Our results support policies regulating PFAA production and use to limit the contamination of Arctic species through long-range transport. https://doi.org/10.1289/EHP13556.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".