Bibliographic record
Abstract
Exposure to air particulate matter (PM) is linked to numerous health effects. In order to improve the understanding of the role of its metallic components, their solubility was examined by using serial short-contact dissolutions totalling 1 h and additional sequential contact periods of 1, 4, and 8 days. The dissolution experiments were performed in solutions containing the main biological electrolytes. ICPMS determinations were used to quantify the dissolved metals. The total compositions were determined after closed vessel microwave digestion. Large variations in the rate and completeness of the dissolutions were observed. Fast and extensive dissolutions within the short-contact time (e.g., Zn, Cd) as well as slow dissolutions persisting during the last contact period (e.g., Ni, Cu, Sb, Pb) were found for smelting emissions. The multi-element determinations also made it possible to identify relationships between dissolution of different metals and define gradual composition changes of residual PM. When comparing with dissolutions performed in de-ionized water, similar major fractions were observed at short-contact time for minor components of smelting or combustion emissions (e.g., V, Ni, Cd), suggesting a preponderance of easily available forms at the surface of the relatively inert particle cores. The use of these time sequential methods may help in (1) modeling metal partitioning in biological media and (2) investigating the causes of adverse effects attributed to air PM.
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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 | 0.000 | 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".