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Record W2936987059 · doi:10.2166/wpt.2019.025

Management of hypochlorite solutions used for water treatment in small drinking water systems

2019· article· en· W2936987059 on OpenAlex
Louis Coulombe, Christelle Legay, Jean Sérodes, Manuel J. Rodríguez

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWater Practice & Technology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicChemical Analysis and Environmental Impact
Canadian institutionsUniversité LavalNatural Sciences and Engineering Research Council of Canada
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Environmental Protection AgencyNational Science Foundation
KeywordsBromateChlorateHypochloriteWater treatmentChlorineDecompositionPerchlorateChlorine dioxideEnvironmental scienceChemistryEnvironmental engineeringWaste managementEngineeringInorganic chemistryIon

Abstract

fetched live from OpenAlex

Abstract Hypochlorite solutions (HSs), also called liquid chlorine, are widely used as disinfectants during drinking water treatment and distribution. However, the decomposition of the hypochlorite ion results in the formation of undesirable inorganic contaminants such as chlorite, chlorate, bromate and perchlorate. While HS decomposition cannot be completely avoided, it can be minimized through applying adequate practices during the purchasing, handling and storage of such solutions. This article presents the results of an investigation of the management of HS in water treatment plants (WTPs) in small municipalities. The data concerning HS management were acquired through field visits and semi-structured interviews with operators and managers of the small WTPs. The information gathered about HS management practices was compared to best management practice guidelines. Results show that practices involving HS differ between WTPs and that there are important gaps in the application of the existing HS management guidelines. The research revealed that the implementation of specific guidelines for the purchasing, handling and storage of HS is difficult for small WTPs due to the lack of human resources, expertise, and education, as well as a lack of infrastructure capacity.

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.

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 score0.070
Threshold uncertainty score1.000

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)0.0010.001

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 head0.017
GPT teacher head0.236
Teacher spread0.218 · 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