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Record W2330808828 · doi:10.2166/wh.2005.0018

Better intervention strategies are needed to reduce the risk of waterborne outbreaks

2005· article· en· W2330808828 on OpenAlexaboutno aff
Edwin E. Geldreich

Bibliographic record

VenueJournal of Water and Health · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsnot available
Fundersnot available
KeywordsWater supplyBusinessAccountabilityIntervention (counseling)Water qualityEnvironmental planningQuality (philosophy)Environmental economicsRisk analysis (engineering)Operations managementEnvironmental resource managementEngineeringEnvironmental engineeringEconomicsEnvironmental science

Abstract

fetched live from OpenAlex

Field investigation of 27 medium to small water systems in Ontario Province has revealed a pattern of deviations in operations that is similar to those reported in the United States over the past 25 years. In this recent Ontario survey of water utilities, the key findings were: (1) a need for full cost pricing of public water supply to consumers; (2) better understanding of water treatment train performance; and (3) a need for management driven accountability to search beyond regulatory minimum requirements for safe water quality. Much of the deteriorating state of operations was a reflection of limited financial base to support an effective management programme. In the survey, small utilities were found to suffer the most from below cost operations which forced the application of a patchwork approach to water treatment and system repairs. Furthermore, small system water plant operators had rare opportunities to take part in workshops on technical issues. These utilities need to partner with the public on water supply issues for financial support to cover daily operations, infrastructure decay and emergency repairs. Ignoring system problems or applying patchwork remedies will eventually lead to unsafe water quality if the current state of affairs is not recognized as a dangerous public health risk.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.281
Teacher spread0.264 · 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 designObservational
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

Citations7
Published2005
Admission routes1
Has abstractyes

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