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Record W3130464505 · doi:10.1520/jte20200308

Modeling Effects of Incising on Flexural Properties of Green Douglas Fir and Western Hemlock Lumber

2020· article· en· W3130464505 on OpenAlexaboutno aff
Jerrold E. Winandy, Butch Bernhardt, Dallin Brooks, Arijit Sinha, Jeffrey J. Morrell

Bibliographic record

VenueJournal of Testing and Evaluation · 2020
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsFlexural strengthDouglas firWestern HemlockStructural engineeringMaterials scienceComposite materialEngineeringForestryGeography

Abstract

fetched live from OpenAlex

ABSTRACT Incising is required in Canadian and US national standards for the treatment of many timber species, especially those from western North America. Incising increases the amount of end-grain exposed to fluid flow, but it also decreases the cross-sectional area and, thus, has the potential to affect mechanical properties. Models have been developed to predict the effects of incising on flexural properties, but correlative data for engineering design adjustment factor(s) for incised and preservative treated lumber and timber are lacking. Current engineering design adjustment factors are primarily based on nominal 2x (38-mm, 1.5-in)-thick lumber tests. In 2018, this fact was recognized and the new design standard allowed for theoretical models to be used for larger materials where appropriate test data were not currently available. This article reports the results of mechanical tests evaluating the effects of incising on flexural properties of nominal 3x and 4x (64- and 89-mm)-thick by 184-mm (7.25-in)-wide Douglas fir and western hemlock lumber. It uses these data to develop potential engineering design adjustment factor(s) for incised nominal 2x (64- and 89-mm)-thick lumber and discusses theoretical modeling to predict those factors.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.213

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 head0.098
GPT teacher head0.258
Teacher spread0.159 · 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 designSimulation or modeling
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

Citations3
Published2020
Admission routes1
Has abstractyes

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