Atmospheric Aerosol Optical Properties in the Persian Gulf
Bibliographic record
Abstract
Aerosol optical depth measurements over Bahrain acquired through the ground-based Aerosol Robotic Network (AERONET) are analyzed. Optical depths obtained from ground-based sun/sky radiometers showed a pronounced temporal trend, with a maximum dust aerosol loading observed during the March-July period. The aerosol optical depth probability distribution is rather narrow with a modal value of about 0.25. The A ngstro m parameter frequency distribution has two peaks. One peak around 0.7 characterizes a situation when dust aerosol is more dominant, the second peak around 1.2 corresponds to relatively dust-free cases. The correlation between aerosol optical depth and water vapor content in the total atmospheric column is strong (correlation coefficient of 0.82) when dust aerosol is almost absent (A ngstro m parameter is greater than 0.7), suggesting possible hygroscopic growth of fine mode particles or source region correlation, and much weaker (correlation coefficient of 0.45) in the presence of dust (A ngstro m parameter is less than 0.7). Diurnal variations of the aerosol optical depth and precipitable water were insignificant. A ngstro m parameter diurnal variability (20%-25%) is evident during the April-May period, when dust dominated the atmospheric optical conditions. Variations in the aerosol volume size distributions retrieved from spectral sun and sky radiance data are mainly associated with the changes in the concentration of the coarse aerosol fraction (variation coefficient of 61%). Geometric mean radii for the fine and coarse aerosol fractions are 0.14 m (std dev 0.02) and 2.57 m (std dev 0.27), respectively. The geometric standard deviation of each fraction is 0.41 and 0.73, respectively. In dust-free conditions the single scattering albedo (SSA) decreases with wavelength, while in the presence of dust the SSA either stays neutral or increases slightly with wavelength. The changes in the A ngstro m parameter derived from a ground-based nephelometer and a collocated sun photometer during the initial checkout period were quite similar.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".