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Record W4255646247 · doi:10.32920/ryerson.14644668

Managing threats to small drinking water systems in Ontario : a risk based approach

2021· preprint· en· W4255646247 on OpenAlexaboutno aff
Wendy Johnson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessWater safetyRisk managementEnvironmental planningRisk assessmentSafe Drinking Water ActRisk analysis (engineering)Economic JusticeEnvironmental healthEnvironmental resource managementEnvironmental scienceComputer securityComputer scienceWater qualityPolitical scienceMedicineFinanceLaw

Abstract

fetched live from OpenAlex

Ultimately the goal of Justice O'Connor's recommendations from the Walkerton inquiry was "to ensure that Ontario's drinking water system deliver water with a level of risk so negligible that a reasonable person would feel safe drinking the water" (O'Connor, 2002a, 5). Following the implementation of Justice O'Connor's recommendations, concerns were raised regarding the management of small drinking water systems using the same stringent rules that were used for municipally-run water systems. Recommendations have focused on the need for risk assessment when managing the threats to small drinking water systems; however no such system has yet been developed in Ontario. A risk-based approach has been developed that would ensure drinking water protection activities are targeted to items that posed the greatest risk to water systems, resulting in more efficient protection efforts. The creation of such a risk-based program can be used to accurately identify significant threats to a water system and result in the effective management of health threats from small drinking water systems.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.107
Threshold uncertainty score0.554

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0090.004
Scholarly communication0.0080.004
Open science0.0030.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.031
GPT teacher head0.244
Teacher spread0.213 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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