Atmospheric Arsenic (As) Concentrations in Different Countries During 2000–2011
Bibliographic record
Abstract
This review study discusses total average concentrations of arsenic (As) in PM10 and PM2.5 in different countries, which included Serbia, Taiwan, Canada, Spain, China, Portugal-Sines, Greece, Korea, France, Hong Kong, Shanghai, Finland, Scotland, Switzerland, United States (Southern California), Italy (Venice) during 2000–2011. Generally, the main sources for As in ambient air in different countries were copper smelters, traffic exhaust, coal use, industrial activities, petrochemical plant, incinerator plants, domestic heating, ship traffic, burning biomass, incinerator emissions, agriculture, mining industry, and foundries. The data show that ambient air As concentrations in PM10 in Serbia in 2000 were the highest while those in PM10 in the United States (Southern California) in 2009 were ranked the lowest. The data also indicate that ambient air concentrations in PM2.5 in Shanghai in 2008 were the highest while those in Greece in 2003 were the lowest. Average ambient air concentrations in PM10 decreased significantly during 2000–2010 in Serbia and Spain. Finally, average ambient air concentrations in PM10 decreased while ambient air particle bound As (As(p)) concentrations in PM2.5 increased during period 2000–2010 and then decreased during 2000–2010.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".