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Record W3088299599 · doi:10.1139/cjce-2020-0167

Mechanical properties of bamboo after exposure to low temperatures

2020· article· en· W3088299599 on OpenAlexafffundvenue
Isa-Bella Leclair, Martin Noël

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBamboo properties and applications
Canadian institutionsUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsBambooBuilding materialUltimate tensile strengthPhyllostachys edulisMaterials scienceCompressive strengthEnvironmental scienceComposite materialGeotechnical engineeringGeology

Abstract

fetched live from OpenAlex

Interest in bamboo as a building material has recently expanded beyond regions traditionally associated with its use. Advantages of bamboo over conventional building materials include a short harvesting time and competitive mechanical properties. This research evaluates the extent to which colder climates may affect the viability of bamboo as a building material in countries with shorter construction seasons. Phyllostachys edulis (Moso) bamboo samples were subjected to freeze–thaw (FT) cycles under wet and dry conditions, and over 100 small-scale tests were performed. Analysis of variance (ANOVA) tests suggest that FT cycles did not significantly affect tensile properties or shear strength perpendicular-to-culm, nor the mechanical properties of samples tested in dry conditions. However, FT in wet conditions resulted in a statistically significant reduction in compressive and shear parallel-to-culm strength of 18% and 21%, respectively. The results suggest that bamboo may be a potentially viable building material in colder climates if protected against excessive moisture ingress.

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 score0.507
Threshold uncertainty score0.834

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 head0.013
GPT teacher head0.153
Teacher spread0.140 · 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

Citations5
Published2020
Admission routes3
Has abstractyes

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