Validation of daily erythemal doses from Ozone Monitoring Instrument with ground‐based UV measurement data
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 Dutch‐Finnish Ozone Monitoring Instrument (OMI) on board the NASA EOS Aura spacecraft is a nadir viewing spectrometer that measures solar reflected and backscattered light in a selected range of the ultraviolet and visible spectrum. The instrument has a 2600 km wide viewing swath and it is capable of daily, global contiguous mapping. The Finnish Meteorological Institute and NASA Goddard Space Flight Center have developed a surface ultraviolet irradiance algorithm for OMI that produces noontime surface spectral UV irradiance estimates at four wavelengths, noontime erythemal dose rate (UV index), and the erythemal daily dose. The overpass erythemal daily doses derived from OMI data were compared with the daily doses calculated from the ground‐based spectral UV measurements from 18 reference instruments. Two alternative methods for the OMI UV algorithm cloud correction were compared: the plane‐parallel cloud model method and the method based on Lambertian equivalent reflectivity. The validation results for the two methods showed some differences, but the results do not imply that one method is categorically superior to the other. For flat, snow‐free regions with modest loadings of absorbing aerosols or trace gases, the OMI‐derived daily erythemal doses have a median overestimation of 0–10%, and some 60 to 80% of the doses are within ±20% from the ground reference. For sites significantly affected by absorbing aerosols or trace gases one expects, and observes, bigger positive bias up to 50%. For high‐latitude sites the satellite‐derived doses are occasionally up to 50% too small because of unrealistically small climatological surface albedo.
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 | 0.003 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it