Aerosol scattering as a function of altitude in a coastal environment
Bibliographic record
Abstract
An optical closure study was carried out on the basis of measurements taken during five research flights in October 2003 over the waters surrounding Nova Scotia. Measurements of aerosol size spectra were made using a variety of instruments, and the size‐segregated chemical signature was determined with an Aerodyne Aerosol Mass Spectrometer. The aerosol scattering and backscattering coefficients were determined with an integrating nephelometer at three visible wavelengths. At a wavelength of 550 nm and at altitudes less than 1000 m, the mean total scattering coefficient of the dry in‐cabin aerosol is 26 Mm−1, with a standard deviation of 10 Mm−1, while the mean backscattering coefficient is 1.7 Mm−1 with a standard deviation of 0.8 Mm−1. On the basis of data from instruments within the cabin, closure between the directly measured and calculated total scattering coefficients is attained for more than 70% of cases, but is not attained for the backscattering coefficients. Coarse particles are found to account for roughly half of the total scattering and 70% of the backscattering for altitudes up to ∼1000 m. The scattering contribution from coarse particles is found to account for approximately 65% of the total scattering and 88% of the backscattering when calculated on the basis of measurements taken outside of the aircraft, which are not subject to inlet losses for larger particles.
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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 | 0.000 | 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".