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Record W4244333275 · doi:10.1080/10408690091189293

Membrane Processing of Fruit Juices and Beverages: A Review

2000· review· en· W4244333275 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

VenueCritical Reviews in Food Science and Nutrition · 2000
Typereview
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsUltrafiltration (renal)MicrofiltrationReverse osmosisElectrodialysisMembraneChemistryFood scienceMembrane technologyFruit juiceFiltration (mathematics)Process engineeringMembrane emulsificationBiochemical engineeringChromatographyPulp and paper industryEngineeringMathematicsBiochemistry

Abstract

fetched live from OpenAlex

Membrane technology for the processing of fruit juices and beverages has been applied mainly for clarification using ultrafiltration and microfiltration, and for concentration using reverse osmosis. The effects of product preparation, membrane selection, and operating parameters are important factors influencing filtration rate and product quality. Technological advances related to the development of new membranes, improvement in process engineering, and better understanding of fruit beverage constituents have expanded the range of membrane separation processes. Developments in novel membrane processes, including electrodialysis and pervaporation, increased the array of applications in combination with other technologies for alternate uses in fruit juices and beverages.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.903
Threshold uncertainty score0.902

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0000.001
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 head0.079
GPT teacher head0.380
Teacher spread0.301 · 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