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Record W2026796098 · doi:10.1080/01496395.2013.809762

Nanofiltration for the Recovery of Low Molecular Weight Polysaccharides and Polyphenols from Winery Effluents

2013· article· en· W2026796098 on OpenAlexaboutno aff
Alexandre Giacobbo, Andréa Moura Bernardes, Maria Norberta de Pinho

Bibliographic record

VenueSeparation Science and Technology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaFundação de Amparo à Pesquisa do Estado do Rio Grande do SulCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsChemistryNanofiltrationMembranePolyphenolPolysaccharideChromatographyPermeationFractionationCelluloseMicrofiltrationBiochemistry

Abstract

fetched live from OpenAlex

A wide range of nanofiltration membranes are investigated for the fractionation of a winery effluent and recovery of the polysaccharides of low molecular weight and of the polyphenols. Permeation experiments were carried out with three laboratory-made cellulose acetate membranes and two commercial membranes: NF270 membrane supplied by Filmtec Corp., Minneapolis, MN (USA) and ETNA01PP membrane supplied by Alfa Laval, Nakskov, Denmark. The five membranes were characterized by the rejection coefficients to a set of reference solutes and evaluated in terms of the rejection coefficients to polysaccharides, polyphenols, conductivity and total organic carbon. The rejection coefficients to polyphenols were overall lower than the ones to polysaccharides, meaning that the polyphenols permeate preferentially through all the membranes. Among these membranes, the NF270 membrane displayed the highest rejection coefficients for all parameters evaluated, with rejections in the order of 93.8% and 99% for polyphenols and polysaccharides, respectively. On the other hand, the ETNA01PP membrane presented the lowest rejection coefficients: around 27% to polyphenols and 72% to polysaccharides.

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)0.0000.001
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 head0.245
Teacher spread0.238 · 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

Citations57
Published2013
Admission routes1
Has abstractyes

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