Observational Studies of Atmospheric Aerosols over Bozeman, Montana, Using a Two-Color Lidar, a Water Vapor DIAL, a Solar Radiometer, and a Ground-Based Nephelometer over a 24-h Period
Bibliographic record
Abstract
Abstract Coordinated observational data of atmospheric aerosols were collected over a 24-h period between 2300 mountain daylight time (MDT) on 27 August 2009 and 2300 MDT on 28 August 2009 at Bozeman, Montana (45.66°N, 111.04°W, elevation 1530 m) using a collocated two-color lidar, a diode-laser-based water vapor differential absorption lidar (DIAL), a solar radiometer, and a ground-based nephelometer. The optical properties and spatial distribution of the atmospheric aerosols were inferred from the observational data collected using the collocated instruments as part of a closure experiment under dry conditions with a relative humidity below 60%. The aerosol lidar ratio and aerosol optical depth retrieved at 532 and 1064 nm using the two-color lidar and solar radiometer agreed with one another to within their individual uncertainties while the scattering component of the aerosol extinction measured using the nephelometer matched the scattering component of the aerosol extinction retrieved using the 532-nm channel of the two-color lidar and the single-scatter albedo retrieved using the solar radiometer. Using existing aerosol models developed with Aerosol Robotic Network (AERONET) data, a thin aerosol layer observed over Bozeman was most likely identified as smoke from forest fires burning in California; Washington; British Columbia, Canada; and northwestern Montana. The intrusion of the thin aerosol layer caused a change in the atmospheric radiative forcing by a factor of 1.8 ± 0.5 due to the aerosol direct effect.
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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 | 0.001 | 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) | 0.000 | 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 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".