Determination of the Source Areas Contributing to Regionally High Warm Season PM<sub>2.5</sub>in Eastern North America
Bibliographic record
Abstract
An ensemble-trajectory analysis technique known as Quantitative Transport Bias Analysis was applied to determine which geographic areas systematically contributed to above- and below-average fine particle mass (PM2.5) over eastern North America. Six-hour average measurements from 12 rural or suburban locations in eastern North America collected using a tapered element oscillating microbalance were individually associated with corresponding 3-day back-trajectories for the warm seasons (May-September) of 2000 and 2001. Much of the populated areas of northeastern North America were implicated in the build-up of PM2.5 to above-average concentrations. The finer structure of the Quantitative Transport Bias Analysis pattern indicated that transport from the Ohio River Valley, particularly the eastern portion of this area, was most often associated with the highest PM2.5 concentrations. In addition, air masses originating over a relatively large area from southeast Ohio to the western part of Virginia and the western Kentucky to central Tennessee area tended to result in relatively high PM2.5 concentrations over northeastern North America. These observation-based findings were consistent with the spatial distribution of the main sulfur dioxide emissions sources and the major oxides of nitrogen point sources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".