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Impact of Media Aging on the Removal of <i>Cryptosporidium</i> in Granular Media Filters

2012· article· en· W2050435594 on OpenAlexaff
Isabelle Papineau, Nathalie Tufenkji, Pierre Servais, Benoît Barbeau

Bibliographic record

VenueJournal of Environmental Engineering · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsPolytechnique MontréalMcGill UniversityNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsAnthraciteCloggingCryptosporidium parvumGrain sizeBiomass (ecology)Filter (signal processing)Particle-size distributionFiltration (mathematics)Particle sizeMaterials scienceEnvironmental engineeringEnvironmental scienceChemistryComposite materialChemical engineeringMicrobiologyBiologyCoalEcologyMathematicsEngineering

Abstract

fetched live from OpenAlex

A study was conducted to assess the impact of media aging on Cryptosporidium parvum transport and retention in granular filters. Column experiments were conducted with sand, anthracite, and granular activated carbon (GAC) filters. All columns possessed an identical filter design and grain size distribution. Oocyst removal was evaluated with both new and aged media. The variation in performance among new media was attributed to the shape of the tested granular media and to the concentration of extractable metals on the surface of collector grains. Media aging significantly increased the treatment performance of sand (26%) and GAC (31%) but had no influence on the performance of anthracite. This enhanced performance of aged media correlated well with the extent of biofilm development. The best removal performance was observed with aged GAC (71±2%), which supported the highest amount of biomass according to polysaccharide and protein measurements. The results of this investigation are of particular interest because they suggest that media aging increases the removal of protozoan parasites in full-scale granular filters.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.256

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 head0.208
Teacher spread0.202 · 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

Citations12
Published2012
Admission routes1
Has abstractyes

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