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Record W4221058531 · doi:10.1617/s11527-022-01911-1

Report of RILEM TC 267—TRM: Improvement and robustness study of lime mortar strength test for assessing reactivity of SCMs

2022· article· en· W4221058531 on OpenAlexaff
Anuj Parashar, Anya Vollpracht, Johannes Haufe, Ruben Snellings, Yuvaraj Dhandapani, Manu Santhanam, Matej Dolenec, Milena Marroccoli, Antonio Telesca, Aneeta Mary Joseph, Natalia Alderete, Nele De Belie, Pawel Durdzinski, Mohsen Ben Haha, Mahipal Kasaniya, M D Thomas, Özlem Çizer, Mathieu Antoni, Serge Sabio, Xuerun Li, R.D. Hooton, Karen Scrivener, Shashank Bishnoi

Bibliographic record

VenueMaterials and Structures · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of TorontoUniversity of New Brunswick
FundersDirektion für Entwicklung und ZusammenarbeitJavna Agencija za Raziskovalno Dejavnost RS
KeywordsLimeMortarFinenessMaterials scienceCalcium hydroxideCuring (chemistry)CementitiousReproducibilityComposite materialCementChemistryMetallurgyChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.009
metaresearch head score (Gemma)0.006
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 score0.021
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.004

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 head0.018
GPT teacher head0.274
Teacher spread0.257 · 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

Citations18
Published2022
Admission routes1
Has abstractno

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