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Record W1998434272 · doi:10.1260/1708-5284.9.1.31

Mechanical and physical properties of particleboard made from two pulp and paper mill secondary sludges

2012· article· en· W1998434272 on OpenAlexafffund
Suying Xing, Bernard Riedl, Ahmed Koubaa, Jianan Deng

Bibliographic record

VenueWorld Journal of Engineering · 2012
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsFPInnovationsUniversité du Québec en Abitibi-TémiscamingueUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlexural strengthMaterials scienceUrea-formaldehydeKraft processComposite materialPulp and paper industryPulp (tooth)Young's modulusPaper millKraft paperWaste managementAdhesiveEngineeringDentistry

Abstract

fetched live from OpenAlex

To investigate environmentally friendly alternatives for sludge disposal, three proportions of secondary sludge (SS) from two pulping processes (Kraft and TMP) were incorporated in the formulation of particleboard manufacturing. A 3 2 factorial design was used where the factors were Urea-formaldehyde (UF) content (5%, 7%, and 9% dry weight of resin per dry weight of particles) and secondary sludge percentage (75%, 100%, and 125% dry weight of SS per dry weight of resin). For each pulping process, 27 panels with SS and 3 control panels (without SS for each resin content) were made for a total of 63 panels. All panels were tested for thickness swell, linear expansion, internal bond strength (IB), flexural modulus of elasticity (MOE) and flexural modulus of rupture (MOR). Results indicated that particleboards made with SS from both pulping processes met the ANSI standards for linear expansion, IB, MOE and MOR. However, none of the tested panels met the standard for thickness swell and adding SS to the formulation affected negatively this property. It was concluded that SS from TMP and Kraft mills can be used to manufacture particleboard panels. However, its' percentage along with other additives' content should be optimized.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentalhigh
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.303

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 communication0.0000.001
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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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

Labeled directly by 2 models reading the full record.

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

Citations11
Published2012
Admission routes2
Has abstractyes

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