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Record W2711395356 · doi:10.5942/jawwa.2017.109.0113

Projecting Financial Capability in Small Canadian Drinking Water Treatment Systems

2017· article· en· W2711395356 on OpenAlexaffabout
Aaron Janzen, Gopal Achari, Mohammed H. Dore, Cooper H. Langford

Bibliographic record

VenueAmerican Water Works Association · 2017
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsBrock UniversityUniversity of Calgary
Fundersnot available
KeywordsRevenueWater supplyPopulationBusinessWater treatmentService (business)Point (geometry)Environmental scienceEnvironmental economicsWater resource managementFinanceEnvironmental engineeringEconomicsMathematicsMarketingEnvironmental health

Abstract

fetched live from OpenAlex

This article determines the minimum service population for which construction and operation of drinking water treatment systems become financially viable by comparing costs with revenue. The feasibility of five solutions that have been implemented in Canada are discussed and tested for financial viability using a novel methodology. Recently published cost equations predict that drinking water treatment plants become financially viable at an estimated population of 920 for surface water sources and 360 for groundwater sources. Intersection points between the cost and revenue curves occur at considerably higher populations than predicted by the Statistics Canada–Calgary Regional Partnership cost equations, highlighting the challenges small systems face in providing drinking water in a sustainable and affordable manner. Decision makers should consider alternative solutions for drinking water supply at unviable service populations, including delivery of water via small diameter “trickle fill” distribution systems, point‐of‐entry and point‐of‐use treatment, and bottle fill stations.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.192
Teacher spread0.183 · 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 designSimulation or modeling
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

Citations5
Published2017
Admission routes2
Has abstractyes

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