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Record W2119808758 · doi:10.1139/w10-047

Inactivation of human adenovirus by sequential disinfection with an alternative ultraviolet technology and monochloramine

2010· article· en· W2119808758 on OpenAlexvenueno aff
Gwy‐Am Shin, Jung‐Keun Lee

Bibliographic record

VenueCanadian Journal of Microbiology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLegionella and Acanthamoeba research
Canadian institutionsnot available
Fundersnot available
KeywordsUltravioletChemistryVirus inactivationWater disinfectionUltraviolet radiationVirologyBiologyMaterials scienceRadiochemistryEnvironmental scienceVirusOptoelectronicsEnvironmental engineering

Abstract

fetched live from OpenAlex

In an effort to reduce human exposure to adenoviruses through drinking water, we determined the effectiveness of sequential disinfection with an alternative ultraviolet (UV) technology (medium-pressure (MP) UV) and monochloramine. The results of this study showed that MP UV was much more effective than traditional UV technology (low-pressure (LP) UV) against human adenovirus 2 (Ad2). Specifically, an inactivation of approximately 3 log10 was achieved by a dose of 40 mJ/cm2 of MP UV compared to ~1 log10 by the same dose of LP UV. However, because of the ineffective inactivation of Ad2 by monochloramine, a very high dose (40 mJ/cm2) of MP UV and a very large Ct99 value (approximately 1200 mg/L.min) was still needed to achieve a significant inactivation (e.g., 4 log10) of Ad2. Also, it appears that the inactivation of Ad2 by monochloramine is not enhanced by prior exposure to MP UV. Overall, the results of this study indicated that, in spite of the enhanced effectiveness of alternative UV technologies on human adenoviruses, sequential disinfection with an alternative UV technology (MP UV) and monochloramine still may not provide adequate inactivation of human adenoviruses - especially at high pH and low temperature - in drinking water treatment processes.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.740

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 head0.008
GPT teacher head0.249
Teacher spread0.241 · 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

Citations8
Published2010
Admission routes1
Has abstractyes

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