MétaCan
Menu
Back to cohort
Record W2158646804 · doi:10.1139/s03-007

Étude du colmatage des membranes en ultrafiltration et en coagulationultrafiltration d'eau de surface

2003· article· en· W2158646804 on OpenAlexvenueno aff
Claude Bouchard, J Sérodes, Mohamed Rahni, Donald Ellis, E Laflamme, Ma Begoña Herrera Rodríguez

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsUltrafiltration (renal)ChemistryFoulingMembraneMembrane foulingCoagulationPermeationChromatographyOrganic matterMembrane permeabilityWater treatmentEnvironmental engineeringEnvironmental science

Abstract

fetched live from OpenAlex

Ultrafiltration (UF) and coagulation–ultrafiltration (CUF) of raw water from the Des Roches Lake were performed and compared in terms of natural organic matter (NOM) removal and in terms of membrane permeability variation. The study was carried out at a laboratory scale with a membrane having a nominal pore size of 0.035 μμm. The removal of NOM by UF was significant (10% to more than 50% for UV absorbance at 254 nm and 0% to more than 30% for TOC) but was also very dependent on operating conditions. Membrane fouling occurred rapidly and was strongly accelerated as the permeate flux increased. Fouling also induced a decrease of NOM removal. Coagulation before UF brought a strong increase of NOM removal (TOC removal higher than 60% and reduction of UV absorbance at 254 nm higher than 80%). It also largely reduced membrane permeability loss even for permeation flux higher than 150 L·h –1 ·m –2 . In the absence of coagulation, and operating with full recirculation of the concentrate, there was a reduction of the SUVA ratio (UV254/TOC) and iron concentration in the feed water. This indicates a preferential deposit of humic substances onto the membranes and shows that iron either plays a role in membrane fouling or could be an indicator of the presence of fouling substances. Key words: ultrafiltration, coagulation, surface water, fouling, organic matter.

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 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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 teacher head, 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

Citations5
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Environmental Engineering and ScienceSame topicMembrane Separation TechnologiesFrench-language works237,207