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Record W3093889008 · doi:10.1111/gwat.13056

Constraining a Flow Model with Field Measurements to Assess Water Transit Time Through a Vadose Zone

2020· article· en· W3093889008 on OpenAlexafffund
Lamine Boumaiza, Romain Chesnaux, Julien Walter, Christine Stumpp

Bibliographic record

VenueGround Water · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVadose zoneGeologySedimentFlow (mathematics)Hydrology (agriculture)Water tableSoil scienceGeotechnical engineeringGroundwaterGeomorphologySoil waterMechanics

Abstract

fetched live from OpenAlex

The modeling of thick vadose zones is particularly challenging because of difficulties in collecting a variety of measured sediment properties, which are required for parameterizing the model. Some models rely on synthetic data, whereas others are simplified by running as homogeneous sediment domains and relying on a single set of sediment properties. Few studies have simulated flow processes through a thick vadose zone using real and comprehensive data sets comprising multiple measurements. Here, we develop a flow model for a 7-m-thick vadose zone. This model, combining the numerical codes CTRAN/W with SEEP/W, includes the measured sediment hydraulic properties of the investigated vadose zone and incorporates the actual climate and subsurface conditions of the study site (precipitations, water-table elevations, and stable isotope data). The model is calibrated by fitting the simulated and measured vertical profiles of water content. Our flow model calculates a transit time of 1 year for the travel of water through the 7-m vadose zone; this estimate matches stable isotope-based results obtained previously for this site. A homogeneous sediment domain flow model, which considers only a single set of sediment properties, produces a transit time that is approximately half the duration of that of the heterogeneous flow model. This difference highlights the importance of assuming heterogeneous material within models of thick vadose zones and testifies to the advantage gained when using real sediment hydraulic properties to parametrize a flow model.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.457
Threshold uncertainty score0.999

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)0.0020.002

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 head0.070
GPT teacher head0.238
Teacher spread0.168 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations17
Published2020
Admission routes2
Has abstractyes

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