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Record W2606722671 · doi:10.1080/20426445.2017.1311525

Influence of furnish parameters on failure mode of oriented strand board under concentrated static load

2017· article· en· W2606722671 on OpenAlexaff
Zheng Chen, Ning Yan, Paul Cooper

Bibliographic record

VenueInternational Wood Products Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOriented strand boardMaterials scienceComposite materialStructural engineeringShear modulusFinite element methodBendingFailure mode and effects analysisYoung's modulusShear (geology)ModulusElasticity (physics)Engineering

Abstract

fetched live from OpenAlex

The influence of oriented strand board (OSB) furnish parameters (strand length, strand width, strand thickness, strand orientation, board density and fines content) on the stress type that initiated failure of OSB under concentrated static load (CSL) was investigated by a mix-level experimental design, test evaluation and finite element (FE) method. The modulus of elasticity (MOE), interlaminar and edgewise shear modulus measured in test were input into the respective FE models developed in this study. By comparing the bending and shear strengths measured in test and related stresses of OSB under CSL simulated by the FE model, the initial failure mode of each type of OSB under CSL was judged. The results showed that stress type causing initial failure under CSL was different if the furnish parameters of OSB panel are different. The developed FE model can predict the behaviour of OSB under CSL.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.014
GPT teacher head0.252
Teacher spread0.238 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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Same venueInternational Wood Products JournalSame topicMaterial Properties and ProcessingFrench-language works237,207