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Record W4321351305 · doi:10.3130/aijt.29.29

EXPERIMENTAL STUDY ON RAPID TEST FOR STATIC MODULUS OF ELASTICITY OF CONCRETE BY 40℃ WARM WATER CURING

2023· article· en· W4321351305 on OpenAlexaff
Shuzo OTSUKA, Yoshihisa NAKATA, Takumi Aramaki, Jinichi SHIBUSAWA, Yutaro UEDA

Bibliographic record

VenueAIJ Journal of Technology and Design · 2023
Typearticle
Languageen
FieldEngineering
TopicConcrete Properties and Behavior
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsAggregate (composite)Materials scienceCuring (chemistry)Composite materialElastic modulusModulusVolume (thermodynamics)Young's modulusGeotechnical engineeringThermodynamicsGeologyPhysics

Abstract

fetched live from OpenAlex

This study investigated the effects of various factors on the applicability of static modulus of elastic to early determination by warm water curing at 40℃. As a result, it was clarified that even if pre-curing time, type of coarse aggregate, Bulk volume of coarse aggregate per unit volume of concrete and concrete temperature were different, static elastic modulus of concrete using various cements could be estimated by the linear equation.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
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)0.0020.000

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 head0.027
GPT teacher head0.251
Teacher spread0.224 · 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
Published2023
Admission routes1
Has abstractyes

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Same venueAIJ Journal of Technology and DesignSame topicConcrete Properties and BehaviorFrench-language works237,207