Characterization of different contaminants and current knowledge for defining chemical mixtures in human milk: A review
Bibliographic record
Abstract
Hundreds of xenobiotics, with very diverse origins, have been detected in human milk, including contaminants of emerging concern, personal care products and other current-use substances reflecting lifestyle. The routes of exposure to these chemicals include dermal absorption, ingestion and inhalation. Specific families of chemicals are dominant among human milk monitoring studies (e.g., organochlorine pesticides, bisphenol A, dioxins), even though other understudied families may be equally toxicologically relevant (e.g., food-processing chemicals, current-use plasticizers and flame retardants, mycotoxins). Importantly, the lack of reliable human milk monitoring data for some individual chemicals and, especially, for complex mixtures, is a major factor hindering risk assessment. Non-targeted screening can be used as an effective tool to identify unknown contaminants of concern in human milk. This approach, in combination with novel methods to conduct risk assessments on the chemical mixtures detected in human milk, will assist in elucidating exposures that may have adverse effects on the development of breastfeeding infants.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".