Identification of the Sources and Geographic Origins of Black Carbon using Factor Analysis at Paired Rural and Urban sites
Bibliographic record
Abstract
Black carbon particles, composed of forms of elemental carbon (EC), contribute significantly to regional and global warming. The origins of EC were examined in southeastern Canada as part of a source apportionment study using positive matrix factorization (PMF), performed on long-term PM2.5 chemical speciation data collected at two paired rural and urban sites. Comparisons of the urban and rural sites revealed a previously unrecognized EC-rich factor that accounted for 41-56% of the total EC in this region. This factor was characterized by the more thermally stable EC fractions that exhibit strong light absorption characteristics. While these EC fractions are often attributed to local diesel emissions, this interpretation was rejected for several reasons. The EC-rich factor was present in similar temporal patterns at both the high-traffic urban and low-traffic rural sites across this 600 km region. The geographic origins of the EC-rich factor were found to be Ohio and Western Pennsylvania regions with heavy industry and multiple coal-based electrical generating stations. The direct radiative forcing due to this EC-rich factor was roughly estimated to be +0.2 W m(-2), which represented a substantial portion of the aerosol induced warming in the region. Thus, this region was impacted by an important unidentified source of EC associated with long-range transport.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".