The dependence of evaporative efficiency of vegetated surfaces on ground cover mass fractions in vegetated soils in mesic ecosystems
Bibliographic record
Abstract
Abstract Bare soil evaporation has been studied extensively, but less is certain regarding how site‐specific features, especially the overstory tree canopy and ground covers, mediate evaporation processes. Inspired by recent advances on modelling bare soil evaporative efficiency (SEE), this study explored SEE over a range of soil substrates and ground cover types, with and without the presence of an overstory canopy in three mesic ecosystems in Canadian Rocky Mountains. A significant relationship was found between the critical soil water content and ground cover mass fractions across various ground cover types, both with and without the presence of an overstory canopy. This relationship is expected to be prevalent across various ecosystems. Moreover, a simple approach for modelling SEE of vegetated surfaces and a correction method to account for below‐canopy SEE is also proposed. The model yields satisfactory simulations, and the approach is expected to be widely applicable, given the strength that its parameters are easily acquired, and its formulations are simple and straightforward. While the model may be particularly suited to mesic ecosystems, the underlying mechanism of SEE suggests that this model can also be applied in dryer conditions. This approach will greatly improve ET parameterization in land‐surface models (LSMs) and increase our knowledge of the global water cycle and ecosystem responses under climate change impacts.
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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 | 0.000 |
| 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) | 0.001 | 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".