MétaCan
Menu
Back to cohort
Record W3034583734 · doi:10.1080/23311916.2020.1770914

Effect of curing methods on the strength of interlocking paving blocks

2020· article· en· W3034583734 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCogent Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCuring (chemistry)PondingCompressive strengthInterlockingMaterials scienceComposite materialStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Interlocking paving blocks (IPB) are an integral part of the pavement infrastructure in developing countries. However, the effect of curing methods on the performance of IPB is vague, and the understanding of this phenomenon will ensure optimum strength and performance for subsequent IPB to be produced. Therefore, this study was carried out to investigate the effect of different curing methods on the compressive strength of IPB. The curing methods evaluated are hot water, open-air, ponding, membrane, earthing and sprinkling methods. Dog-bone samples with a cross-sectional area of 24,401 mm2 were designed and made. The samples are cured using different methods and tested at 7, 14, 21, 28 and 56 days except for those cured in hot water which are tested at 3, 6, 9, 24 and 48 hours after casting. Results from this study indicate that the curing method strongly affects the compressive strength of the IPB. The optimum curing method was found to be the ponding method as it resulted in the highest compressive strength.

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.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.019
GPT teacher head0.283
Teacher spread0.264 · 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