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Record W6957733006 · doi:10.60692/00rsr-w9g05

Report of RILEM TC 267-TRM phase 2: optimization and testing of the robustness of the R3 reactivity tests for supplementary cementitious materials

2022· article· en· W6957733006 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2022
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversity of New BrunswickUniversity of Toronto
FundersEngineering and Physical Sciences Research Council
KeywordsRobustness (evolution)CementitiousTest methodRobustness testingMaterials testingTest data

Abstract

fetched live from OpenAlex

Abstract The results of phase 1 of an interlaboratory test, coordinated by the RILEM TC 267-TRM "Tests for Reactivity of Supplementary Cementitious Materials" showed that the R 3 (rapid, relevant, reliable) test method, by measurement of heat release or bound water, provided the most reliable and relevant determination of the chemical reactivity of supplementary cementitious materials (SCMs), compared to other test methods. The phase 2 work, described in this paper aimed to improve the robustness of the test procedure and to develop precision statements for the consolidated test procedure. The effect of the pre-mixing and mixing conditions, and the impact of the mix design on the test method robustness were assessed and fixed for optimal conditions to carry out the R 3 heat release test. The effect of the drying step was evaluated to define the R 3 bound water test procedure in more detail. Finally, the robustness of the consolidated final test methods was determined by an interlaboratory study to define the precision statements.

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)0.023
metaresearch head score (Gemma)0.014
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 score0.023
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.034
GPT teacher head0.229
Teacher spread0.195 · 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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