Double-layer structure in polar mesospheric clouds observed from SOFIE/AIM
Bibliographic record
Abstract
Abstract. Double-layer structures in polar mesospheric clouds (PMCs) are observed by using Solar Occultation for Ice Experiment (SOFIE) data between 2007 and 2014. We find 816 and 301 events of double-layer structure with percentages of 10.32 and 7.25 % compared to total PMC events, and the mean distances between two peaks are 3.06 and 2.73 km for the Northern Hemisphere (NH) and Southern Hemisphere (SH) respectively. Double-layer PMCs almost always have less mean ice water content (IWC) than daily IWC during the core of the season, but they are close to each other at the beginning and the end. The result by averaging over all events shows that the particle concentration has obvious double peaks, while the particle radius exhibits an unexpected monotonic increase with decreasing altitude. By further analysis of the background temperature and water vapour residual profiles, we conclude that the lower layer is a reproduced one formed at the bottom of the upper layer. 56.00 and 47.51 % of all double-layer events for the NH and SH respectively have temperature enhancements larger than 2 K locating between their double peaks. The longitudinal anti-correlation between the gravity waves' (GWs') potential energies and occurrence frequencies of double-layer PMCs suggests that the double-layer PMCs tend to form in an environment where the GWs have weaker intensities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".