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Record W598218010

50 Years Old and Still Going Strong

2012· article· en· W598218010 on OpenAlexaboutno aff
M D Thomas, R.D. Hooton, Chris Rogers, Benoît Fournier

Bibliographic record

VenueACI Concrete International · 2012
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsFly ashBenchmarkingLand reclamationAlkali–silica reactionEnvironmental scienceForensic engineeringEngineeringWaste managementToxicologyGeographyArchaeologyCementBusinessBiology
DOInot available

Abstract

fetched live from OpenAlex

The use of fly ash for controlling damaging alkali-silica reaction (ASR) was first reported in 1949 by Robert Blanks of the U.S. Bureau of Reclamation. 1 Since then, hundreds of papers have reported the results of laboratory studies on the efficacy of fly ash in this role. While many specifications now permit the use of potentially reactive aggregates, provided a sufficient level of fly ash (or other preventive measure) is used in the concrete, there have been relatively few documented cases of major structures where fly ash has been successfully used together with reactive aggregates. A paper on two such cases, the Nant-y-Moch Dam in Wales, U.K., and the Lower Notch Dam in Ontario, Canada, was published by the primary author when those facilities were about 35 and 25 years old, respectively. 2 The dams were revisited in 2010 when they were about 50 and 40 years old; this article summarizes the performance of these structures with regard to ASR. Studies of performance in the field are essential for confirming the efficacy of preventive measures observed in the laboratory and for benchmarking accelerated laboratory tests intended for the rapid evaluation of such measures. Nant-y-Moch Dam

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score1.000

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 head0.018
GPT teacher head0.263
Teacher spread0.245 · 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.

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

Citations19
Published2012
Admission routes1
Has abstractyes

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