MétaCan
Menu
← Back to cohort
Record W4230460344 · doi:10.32920/ryerson.14656221

A study into the effects of test variables on the results of concrete tests for salt scaling and alkali-silica reaction

2021· preprint· en· W4230460344 on OpenAlexaff
Gregory Richards

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCuring (chemistry)Alkali–silica reactionMaterials scienceScalingReplicateService lifeLeaching (pedology)Alkali metalComposite materialForensic engineeringStructural engineeringEnvironmental scienceEngineeringCementMathematicsChemistryStatistics

Abstract

fetched live from OpenAlex

The ability to accurately determine in-service deterioration of concrete remains an important facet of research. This research aims to develop more reliable laboratory testing methods to better replicate in-service conditions. Two concrete deterioration modes were studied; Salt Scaling (SS) and Alkali-Silica Reaction (ASR). For SS, wrapping slabs in plastic was adopted to provide the same curing environment as curing compound for comparison to standard moist curing. Slabs with and without supplementary cementing materials were tested. The two curing methods produced different scaling results; however, results of tested samples did not change in terms of meeting or failing the acceptance limit. For ASR, modified tests focused on changing sample size to attempt to reduce alkali leaching during testing, and hence produce results that mimic long term performance of actual structures. Cube moulds were designed, manufactured, and used rather than standard prisms. Increase of specimen dimension appear to reduce leaching at 38°C.

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)0.004
metaresearch head score (Gemma)0.019
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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.285
Teacher spread0.262 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicConcrete and Cement Materials Research→French-language works237,207→