Air-vegetation partitioning of polychlorinated biphenyls near a point source
Bibliographic record
Abstract
We investigated polychlorinated biphenyl (PCB) emissions to the environment from a waste treatment and transfer facility over the course of three years. We show that the facility, which undertakes PCB waste consolidation and maintains a low-yield incinerator for products such as light ballasts, acted as a point source for the spatial distribution of PCBs in vegetation. Concurrent air and vegetation sampling was performed to study the relationship between air-vegetation partitioning and the octanol-air partition coefficient (KOA). We show evidence of equilibrium partitioning for lower-chlorinated congeners (log KOA between 7 and 8.5), kinetically limited deposition on plants for intermediate congeners (log KOA between 8.5 and 11), and particle-bound deposition for congeners with high log KOA values (> 11), consistent with the McLachlan partitioning model. From spring to autumn, heavier congeners become much more concentrated in samples farther away from the facility, possibly because of higher temperatures, which enhance dispersal of these congeners. Multivariate principal components analysis showed that PCB composition in vegetation near the emission source most closely resembled the Aroclor mixtures processed by the treatment facility.
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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 | 0.001 | 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".