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Record W1954520847 · doi:10.1002/mame.201400199

Development of Proteinaceous Plywood Adhesive and Optimization of Its Lap Shear Strength

2014· article· en· W1954520847 on OpenAlexaff
Tizazu H. Mekonnen, Paolo Mussone, Phillip Choi, David C. Bressler

Bibliographic record

VenueMacromolecular Materials and Engineering · 2014
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAdhesiveMaterials scienceUrea-formaldehydeRaw materialShear strength (soil)Composite materialTaguchi methodsPulp and paper industryEnvironmental science

Abstract

fetched live from OpenAlex

Abstract The primary goal of this research was to develop a waste protein based adhesive, and to optimize its lap shear strength under dry and wet conditions using Taguchi method. The feedstock for this work was specified risk material (SRM) that is considered the tissue potentially at risk of containing misfolded prions. SRM was hydrolyzed in accordance with government approved techniques and modified with a resin system to develop a renewable adhesive platform. Eight of the nine formulated adhesives met the minimum dry shear strength requirement of urea formaldehyde type adhesive, and three of the formulations also met the soak shear strength requirement. This work demonstrates a general valorization platform for the utilization of animal waste protein as a renewable adhesive feedstock.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.179
Teacher spread0.171 · 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
Published2014
Admission routes1
Has abstractyes

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