Optically Classified Aerosol Properties for Mersin - Erdemli
Bibliographic record
Abstract
Planetary studies have been focused on identifying the chemical components of the earth's atmosphere for over three centuries.Nitrogen, oxygen, carbon dioxide, and noble gases are the major, and trace species are the minor chemicals of the planet earth's atmosphere.They might be found in solid spheroids, liquid droplets, or gases.Whether the specie is an aerosol or a trace gas, many of them are short-lived, necessitating prompt investigation methods to define their spatial and temporal abundance.Remote sensing technologies are robust and prompt solution methods in atmospheric chemistry and physics.Satellite onboard and in-situ sensors provide continuous and human-independent observations even in remote sites.These remotely sensed near real-time data enable scientists to study trends, climatologies, long-range transport of pollutants, and air quality.This study implements a robust aerosol classification method for IMS-METU-Erdemli AERONET station measurements.The station is significant in Levantine Basin and serves a mixture of marine, dust, and anthropogenic aerosols.Results reveal that the ambient air of Erdemli has high pollution levels primarily composed of spheroid particles.Furthermore, these spheroids are more prone to contaminate clouds in smaller sizes, while bigger sizes show the hygroscopicity of the 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 | 0.001 | 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 | 0.001 |
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".