Analysis of GOMOS Ozone Profiles Compared to GMBCD Datasets (bright/dark, star magnitude, star temperature)
Bibliographic record
Abstract
GOMOS ozone profiles were analyzed in a joint contribution of the Ground-based Measurement and Campaign Database (GBMCD) subgroup. In the analysis study 131 collocated ozone profiles of ground-based lidar systems, microwave radiometers, and balloon sondes were used for the validation. We have distinguished between three different parameters which might influence the GOMOS data quality. The pairs of collocated profiles were separated by (1) brightness of the limb during the GOMOS observation, and (2) the magnitude value and (3) temperature of the observed star. For each selection the mean difference between the GOMOS and GBMCD ozone profile was calculated. The GOMOS retrieved ozone profile is strongly affected by the brightness of the limb in which the star occults. Bright limb situations give poor results. Although, in this situation there is an exception for stars with a magnitude value smaller than 1. In that case the results are reasonable between 30 and 50-km altitude, but GOMOS is lower by 10-15%. Twilight limb conditions give better results, but there are still large deviations and it needs further research. Good results come from ozone profiles measured in dark limb situation. Then the bias between 18 and 45-km altitude is within 5 to 10%. The ozone profiles between 45 and 65-km altitude, measured in the dark limb using cold stars, give poor results, but using only hot stars results in a bias lying within 20%. In this case though, it is a significant non-random bias and this suggests a possibility for improvement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".