Coupling water flow and solute transport in a catchment scalehydrological model
Bibliographic record
Abstract
A physically-based distributed hydrological model simulating complex surface– \nsubsurface flow and transport interactions is presented. The subsurface component is \nmodeled by the three-dimensional Richards equation for flow and the classical advection- \ndispersion-reaction equation for transport, solved using finite element/finite volume tech- \nniques. The surface model is based on a path-based (rill flow) diffusion wave equation \nfor both flow and transport, solved using a Muskingum-Cunge scheme. The path-based \nparadigm, together with Leopold and Maddock scaling relations for hydraulic parameter- \nization, allow the same surface model to be used for both overland and channel dynamics. \nA novel approach for resolution of the interactions of water across the land surface, based \non a boundary condition switching algorithm, is extended to the solute flux exchanges. \nThe use of a high resolution finite volume scheme for the advective component of subsur- \nface transport introduces minimal numerical diffusion even in the absence of physical dis- \npersion. An application to the Abdul and Gillham sandbox experiment [1] is presented to \nillustrate the abilities of the model and to demonstrate the influence of surface–subsurface \ndiffusive exchanges on the tracer dynamics of this particular system.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".