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Record W2132722232 · doi:10.1139/w02-049

Hollow-fiber ultrafiltration of<i>Cryptosporidium parvum</i>oocysts from a wide variety of 10-L surface water samples

2002· article· en· W2132722232 on OpenAlexvenueno aff
Ryan C Kuhn, Kevin H. Oshima

Bibliographic record

VenueCanadian Journal of Microbiology · 2002
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsnot available
FundersNew Mexico Water Resources Research Institute, New Mexico State UniversityU.S. Environmental Protection Agency
KeywordsUltrafiltration (renal)TurbidityCryptosporidium parvumCryptosporidiumFiberFilter (signal processing)Filtration (mathematics)Surface waterChromatographyMaterials scienceChemistryBiologyEnvironmental scienceMicrobiologyEnvironmental engineeringComposite materialMathematicsEcology

Abstract

fetched live from OpenAlex

An optimized hollow-fiber ultrafiltration system (50 000 MWCO) was developed to concentrate Cryptosporidium oocysts from 10-L samples of environmental water. Seeded experiments were conducted using a number of surface-water samples from the southwestern U.S.A. and source water from four water districts with histories of poor oocyst recovery. Ultrafiltration produced a mean recovery of 47.9% from 19 water samples (55.3% from 39 individual tests). We also compared oocyst recoveries using the hollow-fiber ultrafiltration system with those using the Envirochek filter. In limited comparison tests, the hollow-fiber ultrafiltration system produced recoveries similar to those of the Envirochek filter (hollow fiber, 74.1% (SD = 2.8); Envirochek, 71.9% (SD = 5.2)) in low-turbidity (3.9 NTU) samples and performed better than the Envirochek filter in high-turbidity (159.0 NTU) samples (hollow fiber, 27.5%; Envirochek, 0.4%). These results indicate that hollow-fiber ultrafiltration can efficiently recover oocysts from a wide variety of surface waters and may be a cost-effective alternative for concentrating Cryptosporidium from water, given the reusable nature of the filter.

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 categoriesInsufficient payload (model declined to judge)
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 score0.155
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0100.000

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 head0.014
GPT teacher head0.198
Teacher spread0.184 · 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.

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

Citations47
Published2002
Admission routes1
Has abstractyes

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