Improvement of Odin/SMR water vapour and temperature measurements and validation of the obtained data sets
Bibliographic record
Abstract
Its long photochemical lifetime makes H 2 O a good tracer for mesospheric dynamics. Temperature observations are also critical to study middle atmospheric dynamics. In this study, we present the reprocessing of 18 years of mesospheric H 2 O and temperature measurements from the Sub-Millimetre Radiometer (SMR) aboard the Odin satellite, resulting in a part of the SMR version 3.0 level 2 data set. The previous version of the data set showed poor accordance with measurements from other instruments, which suggested that the retrieved concentrations and temperature were subject to instrumental artefacts. Different hypotheses have been explored, and the idea of an underestimation of the single-sideband leakage turned out to be the most reasonable one. The value of the lowest transmission achievable has therefore been raised to account for greater sideband leakage, and new retrievals have been performed with the new settings. The retrieved profiles extend between 40–100 km altitude and cover the whole globe to reach 85 ∘ latitudes. A validation study has been carried out, revealing an overall better accordance with the compared instruments. In particular, relative differences in H 2 O mixing ratio are always in the ±20 % range between 40 and 70 km and diverge at higher altitudes, while temperature absolute differences are within ±5 K between 40–80 km and also diverge at higher altitudes.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".