Studies for the Odin sub-millimetre radiometer: I. Radiative transfer and instrument simulation
Bibliographic record
Abstract
The Odin satellite mission will include radiometric measurements of the Earth's atmosphere in a limb-sounding mode, using frequencies between 480 and 580 GHz, with the overall aim of retrieving vertical distributions of atmospheric constituents. The current paper, being one of a three-part series, addresses primarily the modelling of atmospheric radiative transfer and the effect of instrumental properties: the forward model. Such a model is required for the retrieval process and this presentation puts emphasis on refraction, sensor characteristics, systematic model errors, and some implementation aspects. Refraction must be considered below about 15 km and an efficient algorithm to include this effect is presented. Sensor parts treated are the antenna, the side-band filter, and the spectrometer. The forward model is also essential for determining the needed weighting functions. A semi-analytical expression for species-abundance weighting functions is derived. To form a common basis for the article series, a comprehensive formalism is reviewed and general issues, such as the separation between fixed and variable uncertainties, discussed. As a complement to the theoretical characterization, limited to linear situations, the possibility of using repeated simulations is also described. PACS Nos.: 42.68A, 07.07D, 07.57K
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".