Scale dependent prediction of reference evapotranspiration based on Multi-Variate Empirical mode decomposition
Why this work is in the frame
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Bibliographic record
Abstract
This study proposes a novel method for estimation of reference evapotranspiration (ETo) by accounting the time scale of variability using the Multivariate Empirical Mode Decomposition (MEMD). First the ETo and the four predictor variables such as solar radiation, air temperature, relative humidity and wind velocity are decomposed into different intrinsic mode functions (IMFs) and a residue using MEMD. To model ETo, first the modes are modeled separately using the Stepwise Linear Regression (SLR) after identifying the significant predictors at different time scales based on the p-value statistics. Subsequently, the predicted modes are recombined to obtain ETo at the observation scale. The method is demonstrated by predicting the monthly ETo from Stratford station in United States. The results of the study clearly exhibited the superior performance of the proposed MEMD-SLR model when compared with that by M5 model tree, SLR and the EMD-SLR hybrid model. Keywords: Evapotranspiration, Time scale, Decomposition, MEMD, Regression
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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 | 0.000 |
| 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