Analyzing the Relationship Between Ultraviolet Aerosol Index and Aerosol Optical Depth to Fill in Missing Data from a 2021 Wildfire
Bibliographic record
Abstract
Aerosol Optical Depth (AOD) is the measure of aerosols in the air during a certain period of time and indicates relatively hazy parts of the atmosphere. Yet, in many areas, when plotting the AOD on a map, it measures no data. As an alternative, the Ultraviolet Aerosol Index (UVAI), also determines the presence of dust and smoke, two ultraviolet-absorbing aerosols. I hypothesize that there is a way to fill in the “no data” parts of the AOD map using the data already given, including UVAI. To do this, I observed a specific time period (7/10/2021 - 7/22/2021) in which wildfires on the west coast and in Canada were prevalent. Then, I utilized Python libraries to create scatterplots calculating the correlation, as well as a linear regression equation, to analyze the relationship between UVAI and AOD for these dates. Using the linear regression equation, I could plug in UVAI values to get new, potentially correct, AOD values. I replotted the data with these new values and the results were drastically more coverage than the original AOD maps. Therefore, since the correlation between UVAI and AOD was high, the relationship between them was statistically significant enough to fill in the missing data. This new data gives a better understanding of where these wildfires originated and help find points that may have been missed by the photometer.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".