Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning
Why this work is in the frame
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Bibliographic record
Abstract
Shortwave radiation density flux (SRDF) modeling can be key in estimating actual evapotranspiration in plants. SRDF is the result of the specific and scattered reflection of shortwave radiation by the underlying surface. SRDF can have profound effects on some plant biophysical processes such as photosynthesis and land surface energy budgets. Since it is the main energy source for most atmospheric phenomena, SRDF is also widely used in numerical weather forecasting. In the current study, an improved version of the extreme learning machine was developed for SRDF forecasting using the historical value of this variable. To do that, the SRDF through 1981–2019 was extracted by developing JavaScript-based coding in the Google Earth Engine. The most important lags were found using the auto-correlation function and defined fifteen input combinations to model SRDF using the improved extreme learning machine (IELM). The performance of the developed model is evaluated based on the correlation coefficient (R), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash–Sutcliffe efficiency (NSE). The shortwave radiation was developed for two time ahead forecasting (R = 0.986, RMSE = 21.11, MAPE = 8.68%, NSE = 0.97). Additionally, the estimation uncertainty of the developed improved extreme learning machine is quantified and compared with classical ELM and found to be the least with a value of ±3.64 compared to ±6.9 for the classical extreme learning machine. IELM not only overcomes the limitation of the classical extreme learning machine in random adjusting of bias of hidden neurons and input weights but also provides a simple matrix-based method for practical tasks so that there is no need to have any knowledge of the improved extreme learning machine to use it.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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