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Record W2052379270 · doi:10.1029/2009wr007745

Comparison of aquifer characterization approaches through steady state groundwater model validation: A controlled laboratory sandbox study

2010· article· en· W2052379270 on OpenAlex
Walter A. Illman, Junfeng Zhu, Andrew J. Craig, Danting Yin

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWater Resources Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaStrategic Environmental Research and Development ProgramNational Science Foundation
KeywordsAquiferHydraulic conductivityAquifer propertiesPermeameterGroundwater flowGroundwaterSoil scienceGeologyKrigingGroundwater modelFlow (mathematics)Environmental scienceHydrology (agriculture)Geotechnical engineeringMechanicsStatisticsMathematicsGroundwater rechargePhysicsSoil water

Abstract

fetched live from OpenAlex

Groundwater modeling has become a vital component to water supply and contaminant transport investigations. An important component of groundwater modeling under steady state conditions is selecting a representative hydraulic conductivity ( K ) estimate or set of estimates which defines the K field of the studied region. Currently, there are a number of characterization approaches to obtain K at various scales and in varying degrees of detail, but there is a paucity of information in terms of which characterization approach best predicts flow through aquifers or drawdowns caused by some drawdown inducing events. The main objective of this paper is to assess K estimates obtained by various approaches by predicting drawdowns from independent cross‐hole pumping tests and total flow rates through a synthetic heterogeneous aquifer from flow‐through tests. Specifically, we (1) characterize a synthetic heterogeneous aquifer built in the sandbox through various techniques (permeameter analyses of core samples, single‐hole, cross‐hole, and flow‐through testing), (2) obtain mean K fields through traditional analysis of test data by treating the medium to be homogeneous, (3) obtain heterogeneous K fields through kriging and steady state hydraulic tomography, and (4) conduct forward simulations of 16 independent pumping tests and six flow‐through tests using these homogeneous and heterogeneous K fields and comparing them to actual data. Results show that the mean K and heterogeneous K fields estimated through kriging of small‐scale K data (core and single‐hole tests) yield biased predictions of drawdowns and flow rates in this synthetic heterogeneous aquifer. In contrast, the heterogeneous K distribution or “ K tomogram” estimated via steady state hydraulic tomography yields excellent predictions of drawdowns of pumping tests not used in the construction of the tomogram and very good estimates of total flow rates from the flow‐through tests. These results suggest that steady state groundwater model validation is possible in this laboratory sandbox aquifer if the heterogeneous K distribution and forcing functions (boundary conditions and source/sink terms) are characterized sufficiently.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.102
GPT teacher head0.343
Teacher spread0.242 · 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