Characterization of surface solar-irradiance variability using cloud properties based on satellite observations
Why this work is in the frame
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Bibliographic record
Abstract
The variation in surface solar irradiance (SSI) on short timescales has been investigated previously in relation to ground-based observations. Such results are limited to the locality of the observation stations, leading to insufficient knowledge about the spatial distribution of variation features. We propose a method for characterizing variations in SSI using cloud properties obtained from satellite observations. Datasets of cloud properties from satellite observation and SSI from ground-based observation are combined at simultaneous observation points to investigate their relations. The SSI variations are classified statistically into six categories. The cloud properties related to the categorized variation features are then analyzed. From such relations, a statistical discriminant method is used to design a classifier to assign a category to the SSI variation over an area from the cloud properties obtained by satellite observation. The accuracy of classification and feature selection is discussed.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it