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Record W3184473595 · doi:10.1139/cgj-2021-0153

A customized fragment for seepage beneath a dam with a vertical wall

2021· article· en· W3184473595 on OpenAlexvenueno aff
Yijiang Zhang, D. V. Griffiths

Bibliographic record

VenueCanadian Geotechnical Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsIsotropyFragment (logic)Finite element methodGeotechnical engineeringEmbedmentGeologyPermeability (electromagnetism)Flow (mathematics)Type (biology)AnisotropyCover (algebra)Structural engineeringEngineeringGeometryComputer scienceMathematicsMechanical engineeringAlgorithmPhysics

Abstract

fetched live from OpenAlex

Based on the method of fragments for the analysis of steady confined seepage, a customized type E fragment is developed, with results presented in the form of charts for the estimation of seepage quantities and exit gradients under embedded water-retaining structures with a vertical cut-off wall. The type E fragment is shown to be an extension of previously derived type A and type D fragments, allowing for both embedment and a cut-off wall. The charts are generated using finite element analysis and cover both isotropic and anisotropic permeability cases. Validation of the fragment is confirmed by comparison with existing method of fragments and full finite element analysis. The charts are shown to predict the flow rate and exit gradient more precisely than existing methods of fragments. The design charts presented in this paper cover a wide range of confined flow problems of practical interest.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.007
GPT teacher head0.188
Teacher spread0.182 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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Same venueCanadian Geotechnical JournalSame topicDam Engineering and SafetyFrench-language works237,207