Estimating Particulate Matter Concentration over Arid Region Using Satellite Remote Sensing: A Case Study in Makkah, Saudi Arabia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The air quality indicator approximated by satellite measurements is known as an atmospheric particulate loading, which is evaluated in terms of the columnar optical thickness of aerosol scattering. The effect brought by particulate pollution has gained interest among researchers to study aerosol and particulate matter. In this study we presents the potentiality of retrieving concentrations of particulate matter with diameters less than ten micrometer (PM10) in the atmosphere using the Landsat 7 ETM+ slc-off satellite images over Makkah, Mina and Arafah. A multispectral algorithm is developed by assuming that surface condition of study area are lambertian and homogeneous. In situ PM10 measurements were collected using DustTrak aerosol monitor 8520 and their locations were determined by a handheld global positioning system (GPS). The multispectral algorithm model shows that PM10 high during Hajj season compared to other season. The retrieval dataset gives the accuracy > 0.8 of R coefficient value over Makkah, Mina and Arafah. These results provide confidence that the multispectral algorithm PM10 models can make accurate predictions of the concentrations of PM10.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.001 |
| 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 it