MétaCan
Menu
Back to cohort
Record W2486540795

Structural performance of finger-jointed black spruce lumber with different joint configurations

2003· article· en· W2486540795 on OpenAlexaboutno aff
Cecilia Bustos, Robert Beauregard, Mohammad Mohammad, Roger E. Hernández

Bibliographic record

VenueForest Products Journal · 2003
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsJoint (building)Black spruceFinger jointTension (geology)BendingStructural engineeringAdhesiveComposite materialFeatherMaterials scienceUltimate tensile strengthEngineeringEcologyBiology
DOInot available

Abstract

fetched live from OpenAlex

In Eastern Canada, black spruce (Picea mariana (Mill.) B.S.P) is used for producing engineered wood products. However, little information is available on the influence of finger-joint configuration on the structural performance of this species. The purpose of this work was to evaluate the behavior of finger-jointed black spruce for three joint configurations: feather, male-female, and reverse. Isocyanate adhesive was used for all types of joints studied. All of the three joint configurations performed well and strength values were found to meet the Canadian standard requirements. Significant differences were found for bending strength between the three joint profiles. The same trend was observed for tension strength but differences were not statistically significant. The analysis indicated that the feather configuration performs better than male-female and reverse profiles, especially for horizontal structural joints. In tension and bending tests, wood failure was mostly produced along the joint profile but with some failure at the finger roots, indicating an excellent performance of the gluelines.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.017
GPT teacher head0.186
Teacher spread0.168 · 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 source (direct Gemma or distilled Codex), 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

Citations35
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueForest Products JournalSame topicWood Treatment and PropertiesFrench-language works237,207