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ENZYMATIC HYDROLYSIS OF SOYBEAN FOR SOLVENT AND MECHANICAL OIL EXTRACTION

2000· article· en· W2141422853 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Food Process Engineering · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Production and Characterization
Canadian institutionsUniversity of SaskatchewanSaskatchewan Research Council (Canada)
Fundersnot available
KeywordsCellulaseHydrolysisSolventChemistryPressingProteasesExtraction (chemistry)Enzymatic hydrolysisChromatographyOrganic solventEnzymePulp and paper industryOrganic chemistryChemical engineeringBiochemistry

Abstract

fetched live from OpenAlex

ABSTRACT Due to inefficient extractability of its low oil content, soybeans are often bypassed in village‐scale processing. Soygrits, flakes, and expanded collets were hydrolyzed by proteases, cellulases, and pectinases before oil extraction by solvent and static mechanical pressure. Driselase with multi‐enzyme activity and two proteases improved solvent extraction rates but only Driselase enhanced mechanical pressing. Up to 58% of seed oil was pressed from enzyme‐hydrolyzed flakes but 88% was pressed from Driselase‐treated collets. Either pretreatment is a feasible adjunct to mechanical pressing in small batch operations.

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.

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 score0.014
Threshold uncertainty score0.217

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 communication0.0000.000
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 head0.233
Teacher spread0.227 · 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