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Record W6991074082

Evaluation of Hydraulic Conductivity Collected by Various Approaches at a Highly Heterogeneous Field Site

2023· dissertation· en· W6991074082 on OpenAlexafffund

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsUniversity of Waterloo
FundersUniversity of Waterloo
KeywordsGround subsidenceField (mathematics)Measure (data warehouse)OoidLimitingWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

Significant research efforts have been conducted over the last several decades to better understand the groundwater flow and subsurface contaminant transport. It has been found that building a groundwater model for remediation decision-making requires an accurate delineation of spatial variation in hydraulic conductivity (K) and specific storage (Ss). Currently, numerous methods are available for site characterization. Traditional methods such as grain size analyses, permeameter and slug tests can provide point-scale estimates of K, while large-scale estimates from pumping tests are widely used for water-supply and water-quality investigations. However, when the degree of local heterogeneity increases, the necessary number of K increases dramatically, which presents a challenge to conventional methods. As a consequence, Direct Push (DP) based methods have been developed as efficient alternatives to conventional well-based approaches to provide K variability for shallow, unconsolidated aquifers. Hydraulic Profiling Tool (HPT) is one of the novel DP approaches designed for high-resolution site characterization with a test interval of about 1.5 cm. Various site-dependent formulae can be utilized to convert data collected during the HPT surveys into K estimates over a limited range. More recently, inverse modeling approaches of varying degrees of parametrization have become one of the most promising techniques to map hydrostratigraphic spatial variations between boreholes and identify heterogeneity characteristics with a level of detail never before possible. Many comparisons of diverse approaches have been performed, but there is no consensus on which approach yields parameters that are representative for field sites. The main objective of this study is to evaluate K estimates obtained via various site characterizations methods including: (1) grain size analyses; (2) falling head permeameter tests; (3) slug tests; (4) HPT with three different formulae; (McCall and Christy, 2020; Borden et al., 2021; and Zhao and Illman, 2022b) (5) inverse modeling based on a geological zonation approach, and (6) a highly parametrized transient hydraulic tomography (THT) approach. The performance of each approach is first qualitatively analyzed by comparing it with site geology. A 19-layer geological model and forward groundwater model are employed to further assess various methods by simulating seven independent pumping tests that are not used for model calibration under both steady-state and transient-state conditions. Results reveal that the highly parametrized THT analysis with prior geological information yields the best results in model validation under both steady and transient states, and the generated K field revealed the most salient features of inter- and intra-layer heterogeneity. In contrast, traditional methods yield biased prediction of drawdowns, while HPT methods are primarily constraint by the limited range of estimates, especially for low permeable materials.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.027
GPT teacher head0.211
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes2
Has abstractyes

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