Evaluation of PAH diagnostic ratios as source apportionment tools for air particulates collected in an urban-industrial environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A variety of polycyclic aromatic hydrocarbon (PAH) diagnostic ratios were examined as source apportionment tools in the analysis of a PAH data set associated with atmospheric particulate matter collected in an urban-industrial environment. Seventy-six PM(10) samples were collected concurrently at 4 sampling sites over a one-month period in Hamilton, Ontario, Canada, a city of 500 000 people that is home to two integrated steel companies, associated industries and a network of roadways and major highways. Samples collected under well defined meteorological conditions were categorized as being 'upwind' or 'downwind' of the industrial sector. All sample extracts were analyzed for 48 parent PAH, methylphenanthrenes and sulfur-containing aromatics and showed a thousand-fold range of total PAH concentrations (0.23-172 ng m(-3)). Of all PAH diagnostic ratios examined, the two most useful were the anthracene/(anthracene+phenanthrene) and benz[a]anthracene/(benz[a]anthracene+chrysene/triphenylene) ratios. These afforded the best discrimination of samples that had significant industrial impacts. This work is the first example of the use of a linear combination of PAH ratios, coupled with total PAH data and well defined local samples to determine the relative impacts of mobile and industrial emissions in an urban-industrial environment. Use of a linear combination of PAH ratios allowed us to categorize 95% of the data as 'upwind' or 'downwind' of the industrial sector. It is important to determine PAH ratio threshold values based on data from well defined local samples rather than relying on literature values alone.
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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 | 0.003 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it