Coarse mode optical information retrievable using ultraviolet to short‐wave infrared Sun photometry: Application to United Arab Emirates Unified Aerosol Experiment data
Bibliographic record
Abstract
The United Arab Emirates Unified Aerosol experiment provided a unique opportunity for testing aerosol retrieval algorithms in conditions where the optical influence of coarse mode particles was significant. Comparisons between Aerosol Robotic Network (AERONET) Sun photometry retrievals of a previously reported spectral deconvolution algorithm (SDA) and surface‐based microphysical and optical measurements showed levels of correlation suggestive of moderate vertical homogeneity for (extensive) measures of fine and coarse mode aerosol particles and stronger vertical homogeneity for indicators of (intensive) aerosol type. An extension of the SDA into the short‐wave infrared (SDA+) was developed in order to exploit the enhanced coarse mode information available in new AERONET instruments which include a 1.64 μ m channel. Comparisons between values of coarse mode Angstrom exponent ( α c (1.64 μ m)) retrieved from SDA+ and effective (coarse mode) radius derived from AERONET inversions showed moderate but significant correlations. Correlations between coarse mode effective radius derived from α c (1.64 μ m) and surface‐based volume mean diameter estimates underscored the physical significance of the spectral retrievals and suggested moderate vertical homogeneity in terms of coarse mode particle size.
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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 | 0.002 |
| 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 | 0.001 | 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".