MIPAS: an instrument for atmospheric and climate research
Bibliographic record
Abstract
Abstract. MIPAS, the Michelson Interferometer for Passive Atmospheric Sounding, is a mid-infrared emission spectrometer which is part of the core payload of ENVISAT. It is a limb sounder, i.e. it scans across the horizon detecting atmospheric spectral radiances which are inverted to vertical temperature, trace species and cloud distributions. These data can be used for scientific investigations in various research fields including dynamics and chemistry in the altitude region between upper troposphere and lower thermosphere. The instrument is a well calibrated and characterized Fourier transform spectrometer which is able to detect many trace constituents simultaneously. The different concepts of retrieval methods are described including multi-target and two-dimensional retrievals. Operationally generated data sets consist of temperature, H2O, O3, CH4, N2O, HNO3, and NO2 profiles. Measurement errors are investigated in detail and random and systematic errors are specified. The results are validated by independent instrumentation which has been operated at ground stations or aboard balloon gondolas and aircraft. Intercomparisons of MIPAS measurements with other satellite data have been carried out, too. As a result, it has been proven that the MIPAS data are of good quality. MIPAS can be operated in different measurement modes in order to optimize the scientific output. Due to the wealth of information in the MIPAS spectra, many scientific results have already been published. They include intercomparisons of temperature distributions with ECMWF data, the derivation of the whole NOy family, the study of atmospheric processes during the Antarctic vortex split in September 2002, the determination of properties of Polar Stratospheric Clouds, the downward transport of NOx in the middle atmosphere, the stratosphere-troposphere exchange, the influence of solar variability on the middle atmosphere, and the observation of Non-LTE effects in the mesosphere.
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.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".