Early Validation of GOMOS Limb Products Altitude Registration by Backscatter Lidar Using Temperature and Density Profiles
Bibliographic record
Abstract
One basic need for all limb data products is the correct registration of the altitude for each measurement level. The validation of profile products has to comprise beyond the comparison of absolute values also an inspection of the altitude registration assigned to the measurement values. Lidar instruments are particularly suitable to perform this kind of validation due to a very precise determination of altitude as well as an high altitude resolution. Several lidar instruments are included in the validation activities, however, at this early validation stage there are only two contributions to the validation of limb products altitude registration. One contribution is by the University of Bonn Lidar at the Esrange (Sweden) and the other by the York University Lidar at Toronto (Canada) presently run by the Meteorological Service of Canada (MSC). In a campaign lasting from mid July to the end of August validation measurements for Envisat atmospheric products were carried out with the University of Bonn backscatter lidar at the Esrange (68N, 21E) near Kiruna in northern Sweden. Temperature and density profiles of Gomos level 2 products processed with software version GOPR LV2 5.3 were used for comparison with lidar relative density and absolute temperature profiles to obtain information on the altitude registration of Gomos data products. Calculating the cross correlation function of corresponding Gomos and lidar profiles yields altitude-shifts for the maximum cross correlation coefficient. This altitude-shift reveals information on the Gomos altitude-registration. Using the density data for comparison shows a perfect agreement in altitude-registration between
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".