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Record W104000489

Prediction of Internal Bond Strength in Particleboard from Screw Withdrawal Resistance Models

2006· article· en· W104000489 on OpenAlexfundaboutno aff
Kate Semple, Gregory D. Smith

Bibliographic record

VenueWood and Fiber Science (Society of Wood Science and Technology) · 2006
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsEmbedmentEnhanced Data Rates for GSM EvolutionMaterials scienceBond strengthFace (sociological concept)Composite materialStructural engineeringEngineeringAdhesive
DOInot available

Abstract

fetched live from OpenAlex

Density, internal bond (IB) strength, and screw withdrawal resistance (SWR) data from 20 MS and M2 grade particleboards from two Canadian manufacturers were used to examine the correlations between face and edge SWR, and density and IB.SWR data were matched with previously published models for SWR as functions of density or IB, which were only reliable if they contained terms for screw dimensions and embedment depth.There was little or no correlation between the face or edge SWR of particleboards and their density, but sufficiently good correlation with IB (r 2 > 0.7) to support the development of SWR tests as a useful, rapid estimate of IB of particleboard panels.The proposed models are internally calibrated to 5/8-in.-thickboard only and need to be developed and tested on other particleboards.

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 categoriesScience and technology studies
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 score0.072
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
Bibliometrics0.0000.002
Science and technology studies0.0000.003
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.182
Teacher spread0.173 · 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

Citations28
Published2006
Admission routes2
Has abstractyes

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