Aerosol Transport and Source Attribution Using Sunphotometers, Models and In-Situ Chemical Composition Measurements
Bibliographic record
Abstract
Understanding of chemical, physical, and radiative processes-emissions, transport, deposition, and modification of aerosol optical properties due to ageing-is of major importance to global and regional climate simulations and projections as well as health impairment. This paper presents aerosol optical properties retrieved with the Multifilter Rotating Shadowband Radiometers (MFRSRs) and the source attribution based on back trajectories and in situ aerosol chemical composition analysis obtained during the Aerosol Life Cycle Intensive Observational Period at Brookhaven National Laboratory on Long Island, NY, during July and August 2011. The aerosol optical properties retrieved with the MFRSR exhibit excellent agreement with those obtained with a colocated Cimel sunphotometer. Apportioning aerosol optical depths by size modes reveals several episodes of high loading of fine aerosol (diameter less than 2.5 μm). Analysis of optical and physical properties of aerosols as well as their chemical composition obtained by an in situ high-resolution time-of-flight aerosol mass spectrometer together with back trajectories indicates that the principal source of high concentrations of fine aerosols observed during July 18-24 was forest fires in western Canada, consistent with reports by the Canadian Forest Service and satellite observations by the Moderate Resolution Imaging Spectroradiometer (MODIS).
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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 | 0.001 |
| 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".