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Record W2884080616 · doi:10.7451/cbe.2018.60.3.1

Effect of conditioning treatment parameters of cellulases solution on milling characteristics of brown rice

2018· article· en· W2884080616 on OpenAlexvenueno aff
Qiang Zhang, Nian Liu, MIng Tu, Shuangshuang Wang

Bibliographic record

VenueCanadian Biosystems Engineering · 2018
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsnot available
Fundersnot available
KeywordsBrown riceCellulaseConditioningMaterials sciencePulp and paper industryChemistryChemical engineeringFood scienceCelluloseMathematicsEngineeringStatistics

Abstract

fetched live from OpenAlex

A new enzymatic pre-treatment method for improving milling characteristics was studied by spraying cellulases solution on brown rice. The effects of enzymatic pre-treatment parameters on milling characteristics of brown rice were analyzed by response surface methodology. The optimum milling characteristic was obtained under cellulases concentration of 117.4 mg/mL, interval time of 77.2 min and temperature of 35.5 °C. Validation experiments indicated that head rice yield and milling energy consumption were respectively 4.59% higher and 32.95% lower than the values for untreated samples, 1.83% higher and 8.10% lower than those of moisture conditioning treatment. The breaking force and surface structure of the brown rice by enzymatic pre-treatment were studied to reveal the mechanism of change for milling characteristics.

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 score0.003

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 head0.012
GPT teacher head0.204
Teacher spread0.192 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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