Scale dependent prediction of reference evapotranspiration based on Multi-Variate Empirical mode decomposition
Bibliographic record
Abstract
This study proposes a novel method for estimation of reference evapotranspiration ( ET o ) by accounting the time scale of variability using the Multivariate Empirical Mode Decomposition (MEMD). First the ET o 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 ET o , 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 ET o at the observation scale. The method is demonstrated by predicting the monthly ET o 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".