Contamination of Perfluorooctane Sulfonate (PFOS) and Perfluorooctanoate (PFOA) in Water Environment of 21 Cities in 10 Countries
Bibliographic record
Abstract
In this paper, we focus on perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA) in water environment of 21 cities in 10 countries (i.e., Japan, Singapore, Thailand, Malaysia, China, Vietnam, Taiwan, Sweden, Turkey and Canada). 1,160 samples were collected in different water environments (e.g., river, lake, wetland, reservoir, wastewater treatment plant, residence, and sea) from Nov. 2004 to Dec. 2007. The main conclusions obtained in this study are as follows: 1) The average concentration of PFOS in the surface water of rivers was more than 10.0 ng · l-1 in Osaka. The average concentration of PFOS in residential tap water was more than 10.0 ng · l-1 in Bangkok and Taipei. 2) The average concentration of PFOA in the surface water of rivers was more than 1,000 ng · l-1 (median : 48.6 ng · l-1) in Osaka, and it was more than 15 ng · l-1 in Kyoto, Shenzhen, Khon Kaen, and Singapore. The average concentration of PFOA in residential tap water was more than 10 ng · l-1 in Osaka and Okayama in Japan. 3) The number of cities where the difference between river water and tap water was less than 1.0 ng · l-1 in the median concentrations of dissolved PFOS and PFOA was 16 for PFOS and 11 for PFOA, respectively. 4) The dissolved PFOS and PFOA concentrations in effluent were higher than those in influent of most wastewater treatment plants (16/19 for PFOS, 38/45 for PFOA).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".