A Machine Learning - Explainable AI approach to tropospheric dynamics analysis using Water Vapor Meteosat images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Water vapor in the atmosphere plays a crucial role in the energy balance and weather, being responsible for half of the greenhouse effect. Meteorological satellites detect the water vapor and represent it on images, providing important information to understand and forecast the flow dynamics of the General Atmospheric Circulation System. A collection of computational intelligence techniques was used to investigate the structure of a large series of Meteosat (ESA) water vapor band (WV6.2) hourly images from 2009 to 2020. These techniques include the Visual Information Fidelity image quality measure, unsupervised and supervised machine learning and explainable AI methods. Explainable AI methods (XAI) like Permutational Variable Importance, Local Interpretable Model-Agnostic Explanations, Shapley Additive Explanations and Ceteris Paribus profiles, were able to discover temporal variations and changes on the water vapor patterns. The results obtained demonstrate the great potential of ML and XAI in the domain of atmosphere dynamics and weather evolution.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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