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Record W3096480214 · doi:10.14447/jnmes.v23i3.a07

Tensile Performance of a Novel Glue-Laminated Cornstalk Scrimber

2020· article· en· W3096480214 on OpenAlexvenueno aff
Wei Tian, Yongmei Qian, Ruozhu Wang, Yi-Ming Wang

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBamboo properties and applications
Canadian institutionsnot available
FundersPeople's Government of Jilin Province
KeywordsGLUEUltimate tensile strengthComposite numberComposite materialModulusMaterials scienceYoung's modulusStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Glue-laminated cornstalk scrimber is a novel composite to substitute timber. This composite can be prepared in three steps: selecting flawless cornstalks, laying them parallel to grain, and gluing the scrimbers under high pressure. Compared with ordinary timber, glue-laminated cornstalk scrimber excels in the resistance to water, damping, insect, and fire. It is therefore widely recognized as novel eco-friendly and cost- effective composite with great potential in the building industry. The tensile strength of glue-laminated cornstalk scrimber mainly depends on the parallel-to-grain strength of its fibers. The mechanical performance parallel to grain directly determines that of this composite. Hence, this paper carries out experimental analysis on the Young’s moduli and parallel-to-grain tensile strengths of cornstalk scrimber and glue-laminated cornstalk scrimber. The results show that the load-strain curve of glue-laminated cornstalk scrimber basically changed linearly parallel to grain, and the material exhibited stable Young’s modulus and good strength; the glue-laminated cornstalk scrimber had a slightly higher tensile strength than cornstalk scrimber, and could thus replace timber as a building material.

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 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 score0.017
Threshold uncertainty score0.161

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)0.0000.000

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 head0.041
GPT teacher head0.221
Teacher spread0.180 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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