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Record W2461834374 · doi:10.1680/jcoma.15.00070

Rilem activities on alkali–silica reactions: from 1988–2019

2016· article· en· W2461834374 on OpenAlexaboutno aff
Børge Johannes Wigum, Jan Lindgård, Ian Sims, P J Nixon

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Construction Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsSuccessor cardinalAlkali–silica reactionWork (physics)Forensic engineeringTest (biology)Alkali–aggregate reactionEngineeringAggregate (composite)Operations researchMechanical engineeringMaterials scienceMathematicsNanotechnologyGeology

Abstract

fetched live from OpenAlex

Since 1988, the International Union of Laboratories and Experts in Construction Materials, Systems and Structures (Rilem) technical committees (TCs) have been seeking to establish universally applicable test methods for assessing the alkali-reactivity potential of aggregates, and from later on, for concrete mixes. TC 106, in the years 1988 to 2001 focused on accelerated aggregate tests, and presented the findings at the International Conferences on Alkali Aggregate Reactions (ICAAR) in Kyoto in 1989, London in 1992, Melbourne in 1996 and Quebec in 2000. The successor committee TC 191-ARP in the years 2001 to 2006 also included work on diagnosis/appraisal and specification, and presented the findings at the ICAAR in Beijing in 2004. TC 219-ACS in the years 2006 to 2014 introduced work on performance testing and modelling, and presented its findings at the ICAARs in Trondheim in 2008 and in Austin in 2012. The major recommendations were published as a Rilem state-of-the-art report in 2015. In 2014, the TC 258-AAA was established, and it scheduled the completion of work on performance-based assessment for 2019. The preliminary findings will be published at the ICAAR in São Paolo in 2016.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.014
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

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)0.0010.000

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 head0.010
GPT teacher head0.210
Teacher spread0.201 · 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 teacher head, 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

Citations6
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Construction MaterialsSame topicConcrete and Cement Materials ResearchFrench-language works237,207