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Record W3215051760 · doi:10.5772/intechopen.101199

Wood and Engineered Wood Products: Stress and Deformation

2022· book-chapter· en· W3215051760 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIntechOpen eBooks · 2022
Typebook-chapter
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNew Brunswick Innovation Foundation
KeywordsLaminated veneer lumberEngineeringDeformation (meteorology)VeneerOriented strand boardArchitectural engineeringEngineered woodStructural engineeringMaterials scienceCivil engineeringComposite material

Abstract

fetched live from OpenAlex

Wood, as a natural, sustainable, and renewable bio-composite material, has a long history of serving humanity as construction materials. With the advance in technologies, many modern engineered wood products (EWPs) have been invented, produced, and used in construction, such as laminated veneer lumber, oriented strand board, and cross laminated timber. This chapter first introduces the classification, rationales, and pros and cons of EWPs. Secondly, it discusses the stress-related topics, including growth stresses in living trees, the evolution of wood strength from the molecular level to the actual design implementation. Thirdly, this chapter discusses moisture-induced deformation with examples. Finally, it mentions the benefits of using EWPs and their market shares.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.013
GPT teacher head0.179
Teacher spread0.165 · 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