Chemical speciation of mercury associated with airborne\nparticulate matter by thermal desorption coupied with ICP-MS detection
Bibliographic record
Abstract
\nIdentification and quantification of mercury associated with airborne particulate matter are\nimportant in understanding mercury transformation/conversion in the natural environment. This\ninformation can be used to achieve source identification and apportionment of mercury in the\nenvironment and is important in understanding and assessing the risk of mercury to ecological systems\nand to human health. \nA new methodology has been developed for identification and quantification of mercury species\nassociated with atmospheric particulate matter/aerosols. This methodology combines temperaturecontrolled\nthermal desorption for separation of mercury species with ICP-MS for detection and\nquantification. Coal-fly ash spiked with various mercury compounds has been used for testing the new\nmethodology. Samples of airborne particulate matter are collected from urban environment, an industrial\narea and a remote site (Alert, Canada) and are analyzed for mercury species. The results will be\ncompare and discussed in terms of their usefulness for understanding the mechanisms of mercury\ntransformation in the natural environment and for identifying emission sources of mercury.\n
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 | 0.000 |
| 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) | 0.003 | 0.001 |
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".