Estimated spatiotemporal variability of total, direct and diffuse solar radiation across China during 1958–2016
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
The long‐term variability of total, direct and diffuse solar radiation across China during 1958–2016 is investigated based on a ground‐measured daily radiation dataset. Missing data are estimated using a 3‐day average moving window and a backpropagation artificial neural network (BP network). The BP network achieves better estimates of direct ( R 2 = 0.32–0.96) than diffuse radiation ( R 2 = 0.00–0.81). A dimming period during 1958–1990 and a “From Dimming to Brightening” transition between 1990 and 1993 have been detected across China. The declining ratio of direct to diffuse radiation suggests a degrading air quality caused by increasing aerosols in eastern China. To study the aerosol effect on radiation, two empirical models are developed from 2000–2016 using the ground‐measured total radiation, sunshine duration and satellite‐retrieved total aerosol concentration. Both models perform well in the estimate of direct ( R 2 = 0.71–0.89) and diffuse radiation ( R 2 = 0.63–0.95). The increasing total radiation in eastern China since 2000 is mainly contributed by diffuse radiation. Besides, small anthropogenic aerosols can increase diffuse fraction, the proportion of diffuse in total radiation, whereas large natural aerosols may reduce it. The BP network and empirical models exhibit a better agreement in the estimate of direct than diffuse radiation in eastern China, which highlights the impact of aerosols on diffuse radiation in the recent decade.
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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 | 0.001 | 0.001 |
| 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