A Modified Trapezoidal Framework Model for Partitioning Regional Evapotranspiration
Bibliographic record
Abstract
While evapotranspiration (ET) is normally measured as one hydrologic component, evaporation (E) and transpiration (T) result from different physical-biological processes. In the two-source model, a trapezoidal framework has been widely applied in recent years. The key to applying the trapezoidal framework is the determination of the dry/wet boundaries of the land surface temperature-vegetation coverage trapezoid (LST-fc). Although algorithms have been developed to characterize the two boundaries, , there still, however, exists a significant uncertainty near the wet boundary which scatters in a discrete and uneven manner. It thus is difficult to precisely locate the wet boundary. To tackle the problem, a Wet Boundary Algorithm (WBA) was developed in this study and the algorithm was applied in the region of Huang-Huai-Hai plain of China by using Pixel Component Arranging and Comparing Algorithm (PCACA) to retrieve ET from MODIS Data. The latent heat flux (LE) observed by eddy covariance (EC) measurements from China FLUXNET was used to verify the modified model where the coefficient of determination (R2) was found to 0.81 and the root-mean-square-error (RMSE) was 22.8 W/m2. The ratio of transpiration to evapotranspiration (T/ET) varied between 0.5-0.75 over the region of Huang-Huai-Hai plain. The spatial and temporal distribution of ET revealed that agriculture practices had a great influence on the hydrological cycle.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".