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Record W3133877371 · doi:10.3390/ma14051198

Characterizing Mat Formation of Bamboo Fiber Composites: Horizontal Density Distribution

2021· article· en· W3133877371 on OpenAlexaff
Yuan Hu, Mei He, Kate Semple, Mei‐Ling Chen, Hugo Pineda, Chenli Zhou, Chunping Dai

Bibliographic record

VenueMaterials · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBamboo properties and applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBambooComposite materialMaterials scienceComposite numberTaperingPressingFiberMold

Abstract

fetched live from OpenAlex

Bamboo fiber composite (BFC) is a unidirectional and continuous bamboo fiber composite manufactured by consolidation and gluing of flattened, partially separated bamboo culm strips into thick and dense panels. The composite mechanical properties are primarily influenced by panel density, its variation and uniformity. This paper characterized the horizontal density distribution (HDD) within BFC panels and its controlling factors. It revealed that HDD follows a normal distribution, with its standard deviation (SD) strongly affected by sampling specimen size, panel thickness and panel locations. SD was lowest in the thickest (40 mm) panel and largest-size (150 × 150-mm2) specimens. There was also a systematic variation along the length of the BFC due to the tapering effect of bamboo culm thickness. Density was higher along panel edges due to restraint from the mold edges during hot pressing. The manual BFC mat forming process is presented and found to effectively minimize the density variation compared to machine-formed wood composites. This study provides a basic understanding of and a quality control guide to the formation uniformity of BFC products.

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 score0.058
Threshold uncertainty score0.640

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)0.0010.000

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 head0.020
GPT teacher head0.203
Teacher spread0.183 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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