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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".