Validation of daily erythemal doses from Ozone Monitoring Instrument with ground‐based UV measurement data
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.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".