Characterising human exposure to organophosphate ester flame retardants
Bibliographic record
Abstract
Concentrations of selected PFRs were determined in indoor dust from cars, couches, living rooms, offices, and school classrooms in Australia, Canada, Germany, Kazakhstan, and the UK. Significant differences were found between concentrations of PFRs in dust from different microenvironment categories in the same country. Assessment of human exposure via dust ingestion, revealed the majority of exposure to most PFRs occurs in the domestic environment. However, exposure to TDCIPP occurs primarily in cars, and a substantial proportion of the exposure of young children to EHDPP occurs in classrooms. Reassuringly, the exposure estimates in this study are at least 2 orders of magnitude below health based limit values reported in the literature. However, improved characterisation of exposure to TCIPP via inhalation is identified as a research priority. To address the substantial uncertainty in existing values reported for PFR vapour pressures, they were determined experimentally via the GC-RT method. For most target PFRs, results showed good overall agreement with the mean of experimental and \\(in\\) \\(silico\\) literature values. However, values for the chlorinated PFRs exceeded the average of literature values.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".