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Record W1822924132 · doi:10.1139/t11-049

Numerical evaluation of land subsidence induced by groundwater pumping in Shanghai

2011· article· en· W1822924132 on OpenAlexvenueno aff
Shui‐Long Shen, Ye‐Shuang Xu

Bibliographic record

VenueCanadian Geotechnical Journal · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
FundersShanghai Jiao Tong UniversityNational Natural Science Foundation of China
KeywordsGroundwaterHydrogeologyAquiferHydraulic conductivityConsolidation (business)Geotechnical engineeringGroundwater flowGeologySubsidenceGroundwater modelCompressibilityHydraulic headEnvironmental scienceSoil scienceHydrology (agriculture)Soil waterGeomorphologyMechanics

Abstract

fetched live from OpenAlex

To predict the future behavior of land subsidence in Shanghai due to pumping of groundwater, a numerical model is established. In the proposed model, groundwater flow in three-dimensional conditions and soil deformation in one-dimensional conditions are calculated. The model takes into account the multi-aquifer-aquitard hydrogeological condition of the soft deposit of Shanghai. The variation of the coefficient of compressibility and coefficient of hydraulic conductivity of the soils with the consolidation process are simulated. Relationships among land subsidence, groundwater withdrawal volume, and groundwater level are analyzed. Comparison between the measured value and calculated value shows that the model simulates the measured value fairly well. The future of land subsidence behavior due to groundwater withdrawal is predicted and discussed via consideration of the variation of the following parameters in the future 30 years: net withdrawn volume of groundwater, pumping layer, and pumping region.

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: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.033
GPT teacher head0.242
Teacher spread0.209 · 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

Citations369
Published2011
Admission routes1
Has abstractyes

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