Application of tomographic algorithms to Polar Mesospheric Cloud observations by Odin/OSIRIS
Bibliographic record
Abstract
Abstract. Limb-scanning satellites can provide global information about the vertical structure of Polar Mesospheric Clouds. However, information about horizontal structures usually remains limited. This is due to both a long line of sight and a long scan duration. On eighteen days during the Northern Hemisphere summers 2010–2011 and the Southern Hemisphere summer 2011/2012, the Swedish-led Odin satellite was operated in a special mesospheric mode with short limb scans limited to the altitude range of Polar Mesospheric Clouds. For Odin's Optical Spectrograph and InfraRed Imager System (OSIRIS) this provides multiple views through a given cloud volume and, thus, a basis for tomographic analysis of the vertical/horizontal cloud structure. Here we present algorithms for tomographic analysis of mesospheric clouds based on maximum probability techniques. We also present results of simulating OSIRIS tomography and retrieved cloud structures from the special tomographic periods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".