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Record W3125672658 · doi:10.5267/j.esm.2021.1.003

Soil shrinkage and consolidation study on flood embankments in swamp irrigation areas

2021· article· en· W3125672658 on OpenAlexvenueno aff
Lusmeilia Afriani, Gatot Eko Susilo, Sri Nawangrini, Iswan Iswan

Post-publication record

NatureRetraction
ReasonDuplication of/in Article;Euphemisms for Duplication;
Date4/24/2021 0:00
Flagged by OpenAlex?No. Retraction Watch records this, and OpenAlex does not flag it.

Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.

Bibliographic record

VenueEngineering Solid Mechanics · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and construction materials studies
Canadian institutionsnot available
FundersUniversitas Lampung
KeywordsLeveeConsolidation (business)Flood mythGeotechnical engineeringSwampShrinkageHydrology (agriculture)IrrigationEnvironmental scienceGeologyGeography

Abstract

fetched live from OpenAlex

Research in this paper discusses shrinking and consolidation of flood embankments soil in swamp irrigation areas. The flood embankments are made from swampy soil materials. The focus of this research is the reduction of dyke embankment height that occurs due to soil shrinkage and soil consolidation. Investigations about the time of consolidation and land subsidence that occurred on the embankment at certain periods after the embankment established were also carried out in this study. The research sites are some swamp irrigation areas in the Tulang Bawang Watershed, around North-East Lampung, Indonesia. This research was carried out by conducting laboratory tests on soil samples and field observations on the reduction in height of flood embankments in the study area. The research shows that the main cause of total decrease on the embankment is due to linear shrinkage, consolidation of soil under the embankment, an immediate subsidence, and the subsidence of the embankment themselves. Their contribution to total decrease of embankment is 42.51%, 34.48%, 18.32%, and 4.62%, respectively. Results also indicate that the ratio between the percentage of embankment consolidation in downstream area happen faster than the one in upstream area of the river.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.737

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)0.0000.000

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 head0.008
GPT teacher head0.208
Teacher spread0.199 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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