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Record W4393306282 · doi:10.31274/td-20240329-735

The development of the use of magnetic particle rotation in soil ground improvement

2022· dissertation· en· W4393306282 on OpenAlexaboutno aff
Xinyi Jiang

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersIowa State University
KeywordsMagnetic fieldGeotechnical engineeringMaterials scienceCompressibilityMagnetic nanoparticlesSoil waterSoil scienceEnvironmental scienceGeologyPhysicsMechanicsNanotechnology

Abstract

fetched live from OpenAlex

Numerous soil stabilization techniques have been developed to improve the performance of problematic soils (e.g., low strength, high compressibility, etc.) under load. This study evaluates the use of magnetic particles to improve the performance of soil as a potential ground improvement method. The research was conducted in three phases: first, the visualization of the rotation of magnetic particles using a magnetic field within a transparent material acting as a soft clay surrogate; an investigation on the influence of the addition of magnetic particles on the geotechnical properties of Ottawa sand; and third, an investigation on the effect of the rotation of magnetic particles using a magnetic field on the geotechnical properties of Ottawa sand. Three different types of magnetic particles were used in this study: iron filings, graphene flakes, and steel slags. The research demonstrated that by applying a magnetic field, magnetic particles mixed within a soil surrogate and a sandy soil can be rotated to improve soil shear strength performance under loading.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.205
Teacher spread0.192 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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