Bibliographic record
Abstract
The distributions of polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and polycyclic aromatic hydrocarbons (PAHs) in the atmosphere of Toronto, Canada and the surrounding suburban/rural area were examined. A series of temporally- and spatially-distributed air samples was collected over a 1-year period with a high-volume active air sampler at one downtown site and polyurethane foam passive air samplers at 19 sites. Passive sampler air concentrations of ΣPAHs ranged from 0.27 to 51 ng/m³. Concentrations of ΣPCBs ranged from 6.0 to 1300 pg/m³, and concentrations of ΣPBDEs ranged from 0.47 to 110 pg/m³. All compounds exhibited the highest concentrations in the urban core, and lowest concentrations in the surrounding rural areas, however the exact ratio depended on location since concentrations varied considerably within the city. Results from the application of a radial dilution model highlighted the influence of the central business district (CBD) of the city as a source of contaminants to the surrounding environment, however the radial dilution comparison also demonstrated that sources outside the CBD have a significant influence on regional contaminant concentrations. A strong relationship between temperature and partial pressure of the gas-phase PCBs, low molecular weight PBDEs and less-reactive PAHs suggested that their dominant emissions originated from temperature-controlled processes such as volatilization from local sources of PCBs, PAHs and PBDEs at warm temperatures, condensation and deposition of emissions at cold temperatures, and ventilation of indoor air with elevated concentrations. The relationship between temperature and atmospheric PAH concentrations varied along the urban-rural gradient, which suggested that in highly urbanized areas, such as downtown Toronto, temperature-related processes have a significant impact on air concentrations, whereas winter emissions from domestic heating have a greater influence in areas with less impervious surface coverage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".