MétaCan
Menu
Back to cohort
Record W4289333472 · doi:10.1016/j.jmrt.2022.07.096

Parameters estimation and fatigue life prediction of sisal fibre reinforced foam concrete

2022· article· en· W4289333472 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

VenueJournal of Materials Research and Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversité Laval
FundersWenzhou Municipal Science and Technology BureauNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsWeibull distributionMaterials scienceWeibull modulusComposite materialStructural engineeringStatisticsMathematicsEngineering

Abstract

fetched live from OpenAlex

Based on the Glivenko-Cantelli theorem and Weibull distribution functions, the fatigue life of foam concrete with different sisal fibre volume fraction (0–0.3% vol.) was performed. When the two-parameter Weibull function was selected, a linear regression analysis showed that the fatigue life had high correlation between the independent and dependent variables (correlation coefficients above 0.75). But the three-parameter Weibull analysis gave more accurate results (correlation coefficients above 0.90). Then, the corresponding Weibull probability density function distributions of the fatigue lives were compared. Based on the parameters obtained, the fatigue lives of foam concrete with different survival probabilities were analyzed and compared with results taken from the literature. Finally, for specific survival probabilities, the fatigue limit and fatigue life of foam concrete under specified stress level were predicted.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.045
GPT teacher head0.296
Teacher spread0.251 · 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