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Record W2134564726 · doi:10.1177/0731684407075580

Effect of Propylene Carbonate on the Properties of Composite Fiberboard from Wood Fiber Bonded with Renewable Wood Resin

2007· article· en· W2134564726 on OpenAlexaff
Mohini Sain, Arun Ghosh, Indranil Banik

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2007
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceComposite materialPropylene carbonateComposite numberFiberboardMedium density fiberboardFormaldehydeCuring (chemistry)Urea-formaldehydeFiberAdhesiveOrganic chemistry

Abstract

fetched live from OpenAlex

In our previous article, we studied the feasibility of using wood resin in mixture with urea—formaldehyde resin as a binder for the fiberboard composites. In the present article, we have made an attempt to verify the effectiveness of propylene carbonate (PC) as an accelerator on the curing and performance of the composite from the resin mixture. The results demonstrated that the performance of the resulting composite fiberboard cured at lower temperature could be enhanced by addition of propylene carbonate. The composites cured in presence of PC surpassed the minimum requirement of physical properties specified by ANSI-AHA standards. The dosage of PC was optimized to achieve the best combination of composite properties.

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 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 score0.015
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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.

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

Citations3
Published2007
Admission routes1
Has abstractyes

Explore more

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