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Record W7036784128

Coupling water flow and solute transport in a catchment scalehydrological model

2010· article· en· W7036784128 on OpenAlexfundno aff

Bibliographic record

VenueResearch Padua Archive (University of Padua) · 2010
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFondazione Cassa di Risparmio di Padova e Rovigo
KeywordsFlow (mathematics)Finite volume methodCoupling (piping)ScalingAdvectionRichards equationSubsurface flowWater flowBoundary value problemScale (ratio)
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.246
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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