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Record W2012302725 · doi:10.1139/t01-070

Slug tests in a confined aquifer: experimental results in a large soil tank and numerical modeling

2002· article· en· W2012302725 on OpenAlexfundvenueno aff
Robert P. Chapuis, Djaouida Chenaf

Bibliographic record

VenueCanadian Geotechnical Journal · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSlug testAquiferGeotechnical engineeringHydraulic conductivityGeologySlugMechanicsMathematicsGeometrySoil scienceGroundwaterPhysicsSoil water

Abstract

fetched live from OpenAlex

Variable-head (slug) tests in a confined aquifer can be interpreted using different methods that either consider or neglect the influence of the instantaneous deformation of an elastic solid matrix. This paper defines a unified interpretation for slug tests: it is based on the velocity graph describing the conservation equation underlying all methods. If the storativity S has no influence, the velocity graph is a straight line. If S has an influence, the theory considering this influence predicts the graph should be a curve. Numerous slug tests were performed in a large tank containing a confined aquifer. Other tests were used to determine independently the transmissivity T and S values of the confined aquifer which are compared with those obtained from slug tests. The velocity graphs of the slug tests provided straight lines instead of the smooth curves as predicted by the theory. A numerical analysis of these tests in the sand tank was performed using a finite element method. The analysis gave straight lines instead of curves for any S value and therefore confirmed the experimental observation (in velocity graphs) that slug test results do not depend of S and thus cannot be used to determine the S value.Key words: slug test, hydraulic conductivity, storativity, numerical modeling.

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)0.000
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 score0.700
Threshold uncertainty score0.987

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.227
Teacher spread0.209 · 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

Citations40
Published2002
Admission routes2
Has abstractyes

Explore more

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