MétaCan
Menu
Back to cohort
Record W3160848694 · doi:10.1111/wej.12722

Assessment of indigenous surrogate microorganisms for UV disinfection dose verification

2021· article· en· W3160848694 on OpenAlexaff
James E. Blyth, Michael R. Templeton, Sarah‐Jane Court, Cécile Luce, W.L. Cairns, Lucinda Hazell

Bibliographic record

VenueWater and Environment Journal · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsTrojan Technologies (Canada)
FundersEngineering and Physical Sciences Research Council
KeywordsUltravioletIndigenousMicroorganismFood scienceEnvironmental chemistryEnvironmental scienceMicrobiologyBiologyChemistryBacteriaEcologyMaterials science

Abstract

fetched live from OpenAlex

Abstract This study aimed to isolate waterborne heterotrophic organisms indigenous to water treatment processes with suitable ultraviolet (UV) dose–response profiles for estimation of the UV doses delivered by operational UV disinfection systems. The UV dose–response profiles of two isolated organisms, Flavobacterium succinicans and Sphingopyxis chilensis , were determined. S . chilensis has a UV dose–response with potential for use as an indigenous surrogate for Cryptosporidium inactivation, up to 2.2‐logs in the dose range 2–7 mJ/cm 2 of monochromatic UV light at 253.7 nm. F . succinicans was more sensitive and tailing was observed above 4 mJ/cm 2 making it unsuitable for UV dose verification above this dose. As a single species in the water samples used in this study, S . chilensis is unlikely to be present in sufficient numbers for routine use as an indigenous surrogate. However, the Sphingomonadaceae family to which it belongs is abundant in various drinking water sources and warrants further investigation.

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)0.001
metaresearch head score (Gemma)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.009
GPT teacher head0.231
Teacher spread0.221 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueWater and Environment JournalSame topicMicrobial Community Ecology and PhysiologyFrench-language works237,207