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Surface-ground water interactions in numerical simulation: coupling strategies and techniques

2020· dataset· en· W3096217915 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAuthorea · 2020
Typedataset
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsWater Security AgencyUniversity of Regina
Fundersnot available
KeywordsGroundwaterSurface waterCoupling (piping)Surface (topology)Domain (mathematical analysis)Computer scienceComputer simulationScheme (mathematics)Water resourcesEnvironmental scienceGeologySimulationEngineeringMathematicsEnvironmental engineeringGeotechnical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Numerical simulation on groundwater is critical for water resources management. Much research has been conducted in the past using different techniques. Groundwater and surface water should not be treated as isolated components, but rather as interconnected constituents. The interaction between surface and ground water is complex and has never been fully understood. A clear understanding of fundamentals between surface and ground water is essential to conduct groundwater simulation. A comprehensive framework is needed to incorporate physical mechanisms with mathematical models for describing the surface-groundwater interactions. In the present review, up to date coupling strategies and techniques are summarized and compared. Detailed domain models and domain integrated models are reviewed respectively. The advantages and limitations of each technique, including fully coupled scheme and loosely coupled scheme, are presented. The available software using each coupling strategy are listed from previous research. The review will serve as a guidance for future numerical simulation on surface and groundwater interactions.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.482
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.045
GPT teacher head0.326
Teacher spread0.281 · 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