Influence of soil hydrodynamic characteristics variability on surface and subsurface flows at a vegetative buffer strip scale
Bibliographic record
Abstract
The objective of this study is to evaluate the influence of soil hydrodynamic characteristics variability on surface and subsurface flows at a vegetative buffer strip scale, using mechanist modeling. Cathy (CATchment HYdrology, Camporese et al. 2010) is a research partial-differential-equation-based model, solving Richards equation in 3 dimensions for water fluxes in the soil, and a simplified scheme of Navier-Stokes equation for surface runoff. Its particularity is to handle interactions between surface and subsurface, which is a key point concerning water but also solute transport in vegetative filter strips. Balance between runoff and infiltration, flow pathways, water content, are very sensitive to hydrodynamic characteristics, especially saturated hydraulic conductivity (Ksat). This soil property is very difficult to measure and to describe at a fine scale, since it is highly variable spatially in the 3 dimensions. Models described by PDE such as Richards equation need a value of Ksat at each soil layer and each node, though simpler conceptual modeling run with average values of larger cells or storages, using some 'representative Ksat' at a larger scale. This kind of models however can simulate with high quality the processes despite the simplifications they make on parametrization. Using a mechanist and physically-based modeling, we evaluate the influence of Ksat high spatial variability on fluxes, by comparison with observations from an experimental vegetative filter strip. It should allow to understand until which degree of simplification one can describe hydrodynamic characteristics in modeling for more conceptual models.
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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 | 0.001 | 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".