Assessment of the Spatial Distribution of Coplanar PCBs, PCNs, and PBDEs in a Multi-Industry Region of South Korea Using Passive Air Samplers
Bibliographic record
Abstract
Coplanar polychlorinated biphenyls (PCBs), polychlorinated naphthalenes (PCNs), and polybrominated diphenyl ethers (PBDEs) were sampled using polyurethane foam (PUF) disk passive air samplers (PAS) at 19 sites in a heavily industrialized region of South Korea for 6 months (January-July 2006). The levels and spatial distribution of these three chemical groups were investigated to identify potential sources and transport in the study area, which can be divided into five regions: a steel-manufacturing complex, a residential area near the steel complex, a rural area, a semi-industrial area, and a petrochemical-manufacturing complex. Air concentrations (pg x m(-3)) were estimated using an average sampling rate of 3.0 m3 x day(-1) and ranged as follows: coplanar PCBs (0.8-16), PCNs (1.7-35), and PBDEs (3.8-24). The levels of coplanar PCBs and PBDEs were found to be the highest in the steel complex, followed by the petrochemical complex and the semi-industrial area. In addition, a high level of PCNs was measured near a petrochemical-processing plant. However, the residential area near the steel complex and the rural area showed relatively low concentrations of these chemicals, suggesting that the steel and petrochemical industries are probably important sources in the study area, but these potential sources do not strongly influence the surrounding areas.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".