A global database of polybrominated diphenyl ether flame retardant congeners in foods and supplements
Bibliographic record
Abstract
Polybrominated diphenyl ether (PBDE) flame retardants contaminate the food supply yet health effects are uncertain. A global PBDE database was developed to improve diet and disease risk assessments. Congener-specific data from 2002 to 2015 were extracted from 86 articles into a source database representing 32 countries. Geometric mean PBDE concentrations for foods and supplements were derived for 11 congeners individually and combined, and used to calculate means for 27 dietary groups (pg/g ww). Dark or oily fish had the highest data availability, followed by shellfish, eggs, dairy products and dairy fats. Data were less available for white or lean fish, red meat, poultry meat, processed meats, fish oil supplements; 17 groups had very limited data. At the group level, mean ∑ 11 PBDE was extremely high for fish oil supplements (13,862 pg/g) and high for most aquatic groups (462–837 pg/g), poultry liver, poultry fat (1045–1860 pg/g). Moderate groups included white or lean fish, poultry meat, poultry skin, eggs, baked products, red meat fat, red meat liver (115–414 pg/g). Dairy and plant groups had low PBDE concentrations. ∑ 11 PBDE variability was high within most aquatic groups. This database supports assessment of dietary PBDE in multiple jurisdictions and identifies important sources for dietary tool inclusion and analyses.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".