Receptor Modeling for Elemental Source Contributions to Fine Aerosols in New York State
Bibliographic record
Abstract
Fine atmospheric particulate material was collected at five sites in upstate New York and analyzed for its trace element composition by instrumental neutron activation analysis. Of the 3700 daily samples collected over a 2-yr period, 1459 were analyzed for 39 elements, providing a large and detailed data set. Factor analysis (FA) was used to identify potential pollution sources or source regions and to construct inorganic source profiles for each. Following FA, the method of absolute factor scores-multiple linear regression was used to estimate the absolute elemental contribution of each of the identified sources. Factor analysis identified nine sources impacting the sampling region. Seven of these were found to be present in varying degrees among of the sampling sites. The other two sources had more localized impacts and were observed at only one of the sites each. Regional sources (such as the midwestern United States and eastern Canada) and crustal/soil material accounted for the greatest amount of the trace elements measured in the collected material.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".