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Record W4309222816 · doi:10.15376/biores.18.1.255-271

Experimental study of the behavior of box floor with orthogonal ribbed beams by poplar LVL

2022· article· en· W4309222816 on OpenAlexaff
Hongwei Ma, Anlian Wang, Jialong Ji, Yan Liu, Meng Gong

Bibliographic record

VenueBioResources · 2022
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsLaminated veneer lumberStructural engineeringBendingBeam (structure)Materials scienceFlexural strengthSpan (engineering)Orthogonal arrayComposite materialBox girderEngineeringVeneerGirder

Abstract

fetched live from OpenAlex

Considering the unidirectional layout of ribbed beams and simple structure in traditional wooden floors, it is not suitable for large-span wooden buildings. Six groups of floor ribbed beams with plane size of 4.8 m×3.6 m were designed and manufactured by using poplar laminated veneer lumber (LVL), among which five floor specimens were orthogonal ribbed beams and the other one was traditional. A bending performance test was carried out to analyze the influence of different ribbed beam spacing and high span ratio on the mechanical performance of poplar LVL orthogonal ribbed beams, and its results were compared with that of the traditional floor with ribbed beams. The results showed that the box floors with orthogonal ribbed beams had good integrity during the bending process. Moreover, the change of the high span ratio had an important influence on the bending performance of the box floors with orthogonal ribbed beams, and the change of the spacing of the ribbed beams had a relatively small influence on the flexural performance of the box floors with orthogonal ribbed beams. Under the same conditions, the bending performance of the box floors with orthogonal ribbed beams was better than that of traditional floor.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.268

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 head0.008
GPT teacher head0.187
Teacher spread0.179 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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