Polychlorinated Naphthalenes in Great Lakes Air: Assessing Spatial Trends and Combustion Inputs Using PUF Disk Passive Air Samplers
Bibliographic record
Abstract
Passive air samplers made from polyurethane foam (PUF) disks housed in stainless steel chambers were deployed over four seasons during 2002-2003, at 15 sites in the Laurentian Great lakes, to assess spatial and temporal trends of polychlorinated naphthalenes (PCNs). Sampling rates, determined using depuration compounds pre-spiked into the PUF disk prior to exposure, were, on average, 2.9 +/- 1.1 m3 d(-1), consistent with previous studies employing these samplers. PCN air concentrations exhibited strong urban-rural differences-typically a few pg m(-3) at rural sites and an order of magnitude higher at urban sites (Toronto, 12-31 pg m(-3) and Chicago,13-52 pg m(-3)). The high concentrations at urban sites were attributed to continued emissions of historically used technical PCN. Contributions from combustion-derived PCNs seemed to be more important at rural locations where congeners 24 and 50, associated with wood and coal burning, were elevated. Congener 66/67, associated with incineration and other industrial thermal processes, was elevated at two sites and explained by nearby and/or upwind sources. Probability density maps were constructed for each site and for every integration period were shown to be a useful complement to seasonally integrated passive sampling data to resolve source-receptor relationship for PCNs and other pollutants.
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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".