MétaCan
Menu
Back to cohort
Record W4387735467 · doi:10.1139/cgj-2023-0174

Theoretical and field investigation into the vacuum formation in the siphon-vacuum drainage system for soft ground improvement

2023· article· en· W4387735467 on OpenAlexvenueno aff
Junwei Shu, Hongyue Sun, Ni An

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsGeotechnical engineeringDrainageField (mathematics)EngineeringGeologyMathematicsEcology

Abstract

fetched live from OpenAlex

Compared to the traditional drainage consolidation measures for soft ground improvement, the siphon-vacuum drainage method (SVD) offers more advantages, for example, improved drainage efficiency, lower maintenance costs, lower demand for energy and materials, and less environmental impact. Hence, it is expected to be widely applied in soft ground improvement. However, the mechanism of vacuum formation in the system remains unclear. In this study, the vacuum formation mechanism of SVD is investigated based on the theoretical deduction of the variations of the water level and air pressures during the siphon drainage and seepage processes. A theoretical model is developed to describe the vacuum formation, allowing the estimation of the varying air pressure, water level, seepage rate, and discharge rate in the system. Moreover, a field investigation was carried out in Zhoushan, Zhejiang Province, to further validate the correctness of the developed model. This study also provides further insights and suggestions into the field application of SVD for soft ground improvement.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.192
Teacher spread0.185 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207