MétaCan
Menu
Back to cohort
Record W2395882249 · doi:10.2175/106143009x447920

A Potential New Method for Determination of the Fluence (UV Dose) Delivered in UV Reactors Involving the Photodegradation of Free Chlorine

2010· article· en· W2395882249 on OpenAlexafffund
Yangang Feng, Daniel W. Smith, James R. Bolton

Bibliographic record

VenueWater Environment Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsChlorinePhotodegradationFluenceIrradiationWastewaterChemistryUltravioletWater treatmentRadiochemistryMaterials scienceEnvironmental scienceEnvironmental engineeringOptoelectronicsPhotocatalysis

Abstract

fetched live from OpenAlex

In the operation of UV reactors, an important step is to validate if the UV reactor can deliver the design fluence (UV dose) to achieve the required disinfection credit. Free chlorine is used widely in water and wastewater treatment and often is found in the water passing through a UV reactor. Free chlorine is degraded when passing through a UV reactor. This study investigates the potential application of the photodegradation of free chlorine (PFC) to determine the fluence (UV dose) delivered in UV reactors. Using a bench-scale UV reactor, the PFC was investigated at difference UV doses, which also were measured by a biodosimetry method. The results obtained here show that the PFC method has a high correlation with the delivered UV dose (as estimated from the biodosimetry measurements) and is independent of operating conditions, such as flowrate and UV transmittance. In addition, this study indicates that only chloride and chlorate are generated when free chlorine is photodegraded.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.041
GPT teacher head0.329
Teacher spread0.287 · 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
GenreMethods

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

Citations38
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueWater Environment ResearchSame topicWater Quality Monitoring and AnalysisFrench-language works237,207