Updated Climatology of Mesospheric Temperature Inversions Detected by Rayleigh Lidar above Observatoire de Haute Provence, France, Using a K-Mean Clustering Technique
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Bibliographic record
Abstract
A climatology of Mesospheric Inversion Layers (MIL) has been created using the Rayleigh lidar located in the south of France at L’Observatoire de Haute Provence (OHP). Using criteria based on lidar measurement uncertainties and climatological mean gravity wave amplitudes, we have selected significant large temperature anomalies that can be associated with MILs. We have tested a novel approach for classifying MILs based on a k-mean clustering technique. We supplied different parameters such as the MIL amplitudes, altitudes, vertical extension, and lapse rate and allowed the computer to classify each individual MIL into one of three clusters or classes. For this first proof of concept study, we selected k = 3 and arrived at three distinct MIL clusters, each of which can be associated with different processes generating MILs in different regimes. All clusters of MIL exhibit a strong seasonal cycle with the largest occurrence in winter. The four decades of measurements do not reveal any long-term changes that can be associated with climate changes and only show an inter-annual variability with a quasi-decadal oscillation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it