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Record W2725355780 · doi:10.3390/w9070497

Source Water Protection Planning and Management in Metropolitan Canada: A Preliminary Assessment

2017· article· en· W2725355780 on OpenAlexaffabout
Azhar Al Ibrahim, Robert Patrick

Bibliographic record

VenueWater · 2017
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMetropolitan areaEnvironmental planningBusinessWater resourcesEnvironmental resource managementWater qualityLand-use planningRegional planningUrban planningWater resource managementLand useGeographyEnvironmental protectionEnvironmental scienceEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Source Water Protection (SWP) is the process of protecting a drinking water source through land use planning policies and land management activities. The risk of source water contamination is a human health concern even in developed countries such as Canada. Much of the existing SWP literature in the more developed world is centred on small and rural water systems with a focus on capacity needs to support SWP activities and planning. These capacity needs tend to centre on five key elements: political, financial, human, technical and legal. While these contributions have added value to the water resource planning literature in rural areas, there remains a noticeable gap in the literature with respect to SWP activities in metropolitan areas. The purpose of this paper is twofold: first, to report the kinds of source water threats facing metropolitan water systems in Canada; and, second, to explore the utility of the capacity literature with respect to SWP planning in metropolitan Canada.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.994

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0050.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.081
GPT teacher head0.413
Teacher spread0.332 · 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

Citations21
Published2017
Admission routes2
Has abstractyes

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