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Effects of end-pressure on the finger-joint quality of black spruce lumber: A microscopic analysis

2011· article· es· W2137836201 on OpenAlexaff
Cecilia Bustos, Roger E. Hernández, Robert Beauregard, Mohammad Mohammad

Bibliographic record

VenueMaderas Ciencia y tecnología · 2011
Typearticle
Languagees
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsFPInnovationsUniversité Laval
Fundersnot available
KeywordsJoint (building)Quality (philosophy)Black spruceMaterials scienceStructural engineeringBiologyEngineeringPhysicsEcology

Abstract

fetched live from OpenAlex

Efficiency of finger-joints in the Engineered Wood Products is key to the performance of these products. The aim of this research work was to evaluate, by scanning microscopic analysis, the effect of end pressure on the performance of horizontal finger-joint of black spruce (Picea mariana (Mill.) B.S.P) specimens. A feather joint configuration glued with an isocyanate type of adhesive cured at room-temperature was used. The finger-joints were machined at a feed rate of 18.3 m/min, rotational speed of 3500 rpm, and at a feed per knife (chip-load) of 0.86 mm. A single-face glueline application was used at a spread rate of 110 g/m2. The curing time was kept at 24 hours. Six end-pressure levels ranging from 1.38 MPa to 4.82 MPa applied for 20 seconds were investigated. Results showed that cell depth damage increased as end-pressure increased. Joints also showed formation of some air bubbles within the glueline which lead to a reduction in their tensile strength.

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 categoriesMeta-epidemiology (narrow)
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 score0.202
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.035
GPT teacher head0.237
Teacher spread0.202 · 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.

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

Citations17
Published2011
Admission routes1
Has abstractyes

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