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Record W285253683

Estimating Maximum Water Absorption of Wood Fiber/Polymer Fluff Composites

2000· article· en· W285253683 on OpenAlexfundno aff
Sheldon Q. Shi, John Z. Wang

Bibliographic record

VenueWood and Fiber Science (Society of Wood Science and Technology) · 2000
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersBayer CanadaMichigan Technological University
KeywordsComposite materialAbsorption of waterMaterials sciencePolymerSwellingLinear densityFiberAbsorption (acoustics)
DOInot available

Abstract

fetched live from OpenAlex

The objective of this study was to develop a model to estimate the maximum water absorption (MWA) of wood fiberlpolymer Huff composites as a function of polymer Huff content and board density.Polymeric diphenylmethane diisocyanate (PMDI) resin bonded dry-process wood fiberlpolymer Huff composites were used in this study.Six polymer Huff contents (0, 15, 30, 45, 60, and 100%) and four target oven-dry board densities in the range of 0.50-1.00glcm3 were studied.A water immersion test was conducted on these boards.The effect of irreversible thickness swelling after water immersion (TS,) on the estimation of the maximum water absorption was evaluated.It was shown that the irreversible thickness swelling had a quadratic relationship with polymer Huff content and a linear relationship with oven-dry board density.The TS, of the composit~s used in this study was in the range of only 0.04-4.20%,which was negligible in the estimation of maximum water absorption.The prediction of maximum water absorption from the MWA model developed in this study was over 95% accuracy for most of the specimens.The maximum water absorption had a linear relationship with the polymer Huff content and a reciprocal relationship with 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 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)0.001
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 score0.125
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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

Citations12
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueWood and Fiber Science (Society of Wood Science and Technology)Same topicWood Treatment and PropertiesFrench-language works237,207