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Record W2522086591

Quadratic RSM Models of Processing Parameters for Three-Layer Oriented Flakeboards

2007· article· en· W2522086591 on OpenAlexfundno aff
Kaiyuan Wang, Frank Lam

Bibliographic record

VenueWood and Fiber Science (Society of Wood Science and Technology) · 2007
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovations
KeywordsFlakeMaterials scienceComposite materialBendingBucklingYoung's modulusResponse surface methodologyQuadratic equationModulusStructural engineeringOrientation (vector space)Composite numberQuadratic functionGeometryMathematicsEngineeringStatistics
DOInot available

Abstract

fetched live from OpenAlex

Response surface method with central composite design was used to establish quadratic regression models and surface maps to relate panel properties, including static bending modulus of elasticity, modulus of rupture, internal bond strength, and thickness swelling with flake slenderness ratio, flake orientation, and panel density. A robot mat formation system was used to form the panels with predefined processing parameters. Results indicated that nonlinear models capable of including interactions were required to relate flake slenderness ratio, flake orientation, and panel density to panel properties. An optimization model was developed to obtain the best panel performance with respect to the three factors. The optimized combination of the three factors within the experimental range is: 133 for flake slenderness ratio, 8° for surface flake orientation, and 0.62g/cm3 for board density.

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)0.003
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.019
GPT teacher head0.235
Teacher spread0.216 · 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

Citations25
Published2007
Admission routes1
Has abstractyes

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