A vector radiative-transfer model for the Odin/OSIRIS project
Bibliographic record
Abstract
A vector radiative-transfer code has been developed that is able to accurately and efficiently calculate radiance and polarization scattered from Earth's limb. A primary application of this code will be towards generating weighting functions, based on calculated limb radiances, for the retrieval of trace gases (O3, NO2, BrO, OClO, and O4) from the optical spectrograph and infrared imaging system (OSIRIS). OSIRIS is a UVvisible instrument on board the Odin satellite that measures limb-scattered light. This model solves the vector radiative-transfer equation using an iterative technique simultaneously in both plane-parallel and spherical-shell atmospheres. OSIRIS simulated limb radiance and polarization and OSIRIS weighting functions are presented along with a discussion of the numerical solution parameters, model intercomparisons and timings, and necessary model improvements. Overall agreement with other models was found to be very good and model speed is comparable to a fast finite-difference code. A set of OSIRIS reference atmospheres have been compiled for use with radiative-transfer models. Each of the 216 atmospheres (18 latitudes × 12 months) include profiles of air, pressure, temperature, ozone, NO2, BrO, and stratospheric aerosols. PACS Nos.: 42.68-w, 94.10Gb
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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".