Application of the Multimedia Urban Model To Compare the Fate of SOCs in an Urban and Forested Watershed
Bibliographic record
Abstract
A multimedia model has been developed to estimate the dynamics of semivolatile organic compounds (SOCs) in urban areas. The model is based on a Level III fugacity model of Mackay and consists of six compartments: air, surface water, sediment, soil, vegetation, and an organic film that coats impervious surfaces. The model was used to illustrate the effect of impervious surfaces in urban areas by parametrization for downtown, Toronto, Canada, and modification of the same area to simulate forested conditions. With illustrative emissions of PCB homologues to air, the model indicates that most chemicals are lost by advection, with the remainder undergoing air-to-surface (organic film or vegetation) transfer. Under urban conditions chemicals with Log[K(OA)] < 7.5 volatilize from the film into air where they are susceptible to advection and photolytic degradation. Chemicals with Log[K(OA)] > 7.5 are washed off the film to surface waters where they may undergo volatilization, advection, sedimentation, and degradation. Both loss mechanisms from the film increase the overall mobility of SOCs in the urban relative to the forested environment. In forested areas, vegetation more efficiently accumulates gas- and particle-phase SOCs and subsequently transfers them to surface soils, the greatest chemical reservoir, where they are relatively immobile.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".