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Record W1760168924 · doi:10.1139/x2012-041

Bending strength and stiffness of in-grade Douglas-fir and southern pine No. 2 2 × 4 lumber

2012· article· en· W1760168924 on OpenAlexvenueno aff
Joseph Dahlen, Phillip D. Jones, R. Daniel Seale, Rubin Shmulsky

Bibliographic record

VenueCanadian Journal of Forest Research · 2012
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsDouglas firPinus <genus>StiffnessPercentileMathematicsBending stiffnessHorticultureForestryAnimal scienceBotanyMaterials scienceComposite materialBiologyStatisticsGeography

Abstract

fetched live from OpenAlex

Douglas-fir ( Psuedotsuga menziesii (Mirb.) Franco) (DF) and southern pine ( Pinus spp.) (SP) trees are increasingly grown on intensively managed plantation forests that yield excellent growth. Lumber cut from these trees often contains a large percentage of juvenile wood, which negatively impacts its strength and stiffness. Design values of lumber must accurately reflect the available forest resource, and because the design values were determined over 25 years ago, questions exist whether wood quality has declined. To help address this, 1488 samples of commercial-grade No. 2 2 × 4 DF and SP lumber were destructively tested in edgewise bending. Mean stiffness of DF and SP was 11.51 and 10.89 GPa, respectively, comparing favorably with the 11.02 GPa mean design values; however, variation for SP was higher than in previous studies. The nonparametric 5th percentile bending strengths for DF and SP were 8.30 and 9.06 MPa, respectively. Both DF and SP tested at less than their design values, 9.3 and 10.34 MPa, respectively. Stiffness explained 66% and 52%, respectively, of the variability in strength for DF and SP. Because this relationship seems only moderately predictive, it may be prudent to couple stiffness with additional measures of quality when predicting bending 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.956

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 head0.040
GPT teacher head0.271
Teacher spread0.231 · 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 designObservational
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

Citations23
Published2012
Admission routes1
Has abstractyes

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