Factors affecting the levels and pathways of atmospheric brominated flame retardant uptake by humans in different weather conditions
Bibliographic record
Abstract
The health risks posed by atmospheric brominated flame retardants (BFRs) have been widely studied, but there remains a lack of clarity about exposure differences between clear days and haze days. We sampled the total suspended particle (TSP) and gaseous BFRs on clear days in summer, clear days in winter, and haze days in winter in Harbin, China, to investigate the variations in the concentrations and intakes (dermal and inhalation) in the different weather conditions. The concentrations of atmospheric BFRs were highest on haze days in winter (185 pg/m3), followed by clear days in summer (158 pg/m3), and clear days in winter (79.2 pg/m3), and these concentrations were significantly correlated with the concentrations of TSP. The human intake in the different weather conditions followed the pattern of the BFR concentrations, and the human intake of atmospheric BFRs was dominated by inhalation. A comparison of the BFR levels showed that the human intake of gaseous and particulate BFRs varied in the three weather conditions and that the inhalation intake, but not the dermal intake, was influenced under high concentrations of gaseous BFRs with low molecular weight.
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 | 0.000 | 0.000 |
| 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".