Influence of soil spatial variability on surface and subsurface flow 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 flow at a vegetative buffer strip scale, using mecanistic modeling. Cathy (CATchment HYdrology, Camporese et al. 2010) is a research physically based model able to simulate coupled surface/subsurface flow. The evaluation of soil hydrodynamic characteristics variability is based essentially on saturated hydraulic conductivity because of its large spatial variability in the 3 dimensions and its important influence on flow pathways, as well as its high influence on the model output variables. After testing the model sensitivity to some input variables, to the boundary conditions and to the mesh definition, the work focuses on hydraulic conductivity parametrization. The study was first conducted with uniform (by horizons) conductivity domains based on field measurements. In a second step, heterogeneous fields were generated by a statistical tool which allows the user to choose the statistical law (in this case, lognormal or Gauss), the hydraulic conductivity auto-correlation length and the possibility to condition the fields with measured points. With all these different ways to represent spatial variability of hydraulic conductivity, model simulated surface and subsurface fluxes consistent with datasets from artificial run-off experiments on an French wineyard hillslope (Morcille catchment, Beaujolais, France). Model simulations are evaluated and compared to observations on several criteria: consistency, stability, interaction with water table, etc...
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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 | 0.001 | 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) | 0.000 | 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".