Bias determination and precision validation of ozone profiles from MIPAS-Envisat retrieved with the IMK-IAA processor
Bibliographic record
Abstract
Abstract. This paper characterizes vertical ozone profiles retrieved with the IMK-IAA (Institute for Meteorology and Climate Research, Karlsruhe – Instituto de Astrofisica de Andalucia) science-oriented processor from high spectral resolution data (until March 2004) measured by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) aboard the environmental satellite Envisat. Bias determination and precision validation is performed on the basis of correlative measurements by ground-based lidars, Fourier transform infrared spectrometers, and microwave radiometers as well as balloon-borne ozonesondes, the balloon-borne version of MIPAS, and two satellite instruments (Halogen Occultation Experiment and Polar Ozone and Aerosol Measurement III). Percentage mean differences between MIPAS and the comparison instruments for stratospheric ozone are generally within ±10%. The precision in this altitude region is estimated at values between 5 and 10% which gives an accuracy of 15 to 20%. Below 18 km, the spread of the percentage mean differences is larger and the precision degrades to values of more than 20% depending on altitude and latitude. The main reason for the degraded precision at low altitudes is attributed to undetected thin clouds which affect MIPAS retrievals, and to the influence of uncertainties in the water vapor concentration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".