Evaluation of Solar Radiation Prediction Models in North America
Why this work is in the frame
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Bibliographic record
Abstract
Solar radiation data at the earth's surface ( R s , MJ m −2 d −1 ) are not typically recorded at weather stations, but they may be predicted from other meteorological measurements. For one location, Keiser, AR, we developed an empirical equation for predicting R s . The mechanistic models of Hargreaves–Samani (HS) and two forms of the Bristow–Campbell model, described by Thornton and Running (TR) and Weiss et al. (WS), were also evaluated for predicting R s at 13 sites, covering a 23° range in latitude and a 42° range in longitude. For the HS, TR, and WS models, we used coefficients as they were originally published, and for the HS model, a site‐specific coefficient (HS‐SS) was derived and evaluated for each site. Regression of predicted vs. observed R s values using the empirical equation for Keiser gave r 2 values (0.77) similar to the best of the mechanistic models. The HS‐SS model had the lowest root mean square error of 3.50 MJ m −2 d −1 , followed by the TR (3.56), the HS (3.86), and the WS (4.33) models. Predicted vs. observed values gave r 2 values ranging from 0.72 (TR model) to 0.56 (WS model). There was a slight superiority of the TR model over the HS‐SS and HS models. Similar fits ( r 2 > 0.87) and errors were found among the TR, HS‐SS, and HS models when R s values were averaged over a 7‐d period, and it was concluded that these three models provided accurate and precise R s estimations for our sites without further model modification.
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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 | 0.000 |
| 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