Validation of ozone data from the Superconducting Submillimeter‐Wave Limb‐Emission Sounder (SMILES)
Bibliographic record
Abstract
The Superconducting Submillimeter‐Wave Limb‐Emission Sounder (SMILES) onboard the International Space Station provided global measurements of ozone profiles in the middle atmosphere from 12 October 2009 to 21 April 2010. We present validation studies of the SMILES version 2.1 ozone product based on coincidence statistics with satellite observations and outputs of chemistry and transport models (CTMs). Comparisons of the stratospheric ozone with correlative data show agreements that are generally within 10%. In the mesosphere, the agreement is also good and better than 30% even at a high altitude of 73 km, and the SMILES measurements with their local time coverage also capture the diurnal variability very well. The recommended altitude range for scientific use is from 16 to 73 km. We note that the SMILES ozone values for altitude above 26 km are smaller than some of the correlative satellite datasets; conversely the SMILES values in the lower stratosphere tend to be larger than correlative data, particularly in the tropics, with less than 8% difference below ~24 km. The larger values in the lower stratosphere are probably due to departure of retrieval results between two detection bands at altitudes below 28 km; it is ~3% at 24 km and is increasing rapidly down below.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".