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Record W3182516089 · doi:10.5539/jmsr.v10n1p28

Canola Meal Adhesive for the Production of Wood Fiber Insulation Boards Using Hot-Air/Hot-Steam-Process

2021· article· en· W3182516089 on OpenAlexvenueno aff
Kolja Ostendorf, Julian Haerkötter, Markus Euring

Bibliographic record

VenueJournal of Materials Science Research · 2021
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
FundersDeutsche Forschungsgemeinschaft
KeywordsCanolaMaterials scienceAbsorption of waterComposite materialAdhesiveCuring (chemistry)MealSteam explosionPulp and paper industryFood scienceChemistry

Abstract

fetched live from OpenAlex

Canola meal as a by-product from the vegetable oil production provides a protein-rich material which is available in large quantities but with limited areas for application. The objective of this study was to investigate the possibility of utilizing canola meal adhesive for the production of wood fiber insulation boards (WFI) using the hot-air/hot-steam-process. WFI with two different thicknesses (40/60 mm) and different densities (110/140/160/180 kg/m³) were manufactured. The testing focused on their physical-mechanical properties such as internal bond strength (IB), compressive strength (CS) and short-term water absorption (ST-WA) measured according to European standards. For a better understanding of the material and curing dynamics, the canola meal was analyzed on its protein content, lignin and pentosane content as well as its extractives content using hot water, cold water and successive extraction. Using a canola meal based adhesive resulted in promising results for IB and CS up to density of 140 kg/m³. Nonetheless, there is place for improvement for the ST-WA.

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)0.009
metaresearch head score (Gemma)0.004
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 score0.006
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.081
GPT teacher head0.397
Teacher spread0.316 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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