An effective formulation for estimating wetland surface energy fluxes from weather data
Bibliographic record
Abstract
In modelling evapotranspiration, the need for land surface variables including ground heat fluxes (G), surface temperature (Ts), surface relative humidity (RHs) and surface resistance often present a challenge due to land heterogeneity and limited measurements. This study introduces a simple formulation rooted in the shared physical basis of the maximum entropy model (MaxEnt), the Relative Humidity at Equilibrium (ETRHEQ) method, and the Surface Flux Equilibrium (SFE) method, and it estimates sensible (H) and latent fluxes (LE) in wetlands without requiring land surface variables or site-specific calibration, except for an assumed vegetation height. Further, it effectively estimates LE from half-hourly to monthly scales in FLUXNET and AmeriFlux wetland sites. While its performance in estimating H is less satisfactory due to loosely constrained boundary conditions, it shows promising potential for simultaneously and precisely estimating LE, H, G, Ts, and RHs from weather data in various ecosystems.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".