MétaCan
Menu
Back to cohort
Record W4282967285 · doi:10.1139/cjce-2021-0621

Laboratory assessment of capillary rising in cement- and lime-treated engineered loess

2022· article· en· W4282967285 on OpenAlexaffvenue
Shenglin Wang, Xiaoyuan Li, Shunde Yin

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of ChinaChina Railway
KeywordsLoessCapillary actionLimeGeotechnical engineeringWater contentSoil waterMoistureEnvironmental scienceCementSoil scienceGeologyMaterials scienceComposite materialMetallurgyGeomorphology

Abstract

fetched live from OpenAlex

The capillary rising is one of the most common phenomena in fine-grained soils. However, research is limited for capillary actions in cementitiously treated engineered soils. In this study, a soil column test was performed for the free capillary rising in compacted loess and modified loess samples. Moisture content of compacted soil samples was measured and analyzed to determine capillary height and rising rate. Results indicated that there was a considerable capillary rising in loess and lime-modified loess, while the capillary rising in cement-treated loess was significantly restrained. Current models are not appropriate for the rate and height predictions of capillary rising in cementitiously treated loess, especially in early ages. Therefore, a logarithmic form correlation was suggested and validated. In addition, salt migration effect and microscopic properties of soils after capillary rising were evaluated. Overall, the paper presented an experimental study and prediction on the capillary rising behavior of treated and recompacted engineered loess.

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 score0.004
Threshold uncertainty score0.007

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 communication0.0000.000
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 head0.005
GPT teacher head0.181
Teacher spread0.176 · 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

Citations8
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicSoil and Unsaturated FlowFrench-language works237,207