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Record W2998774476 · doi:10.1061/9780784481653.010

Integrated Asset Management Planning of Wastewater Collection and Treatment Systems

2018· article· en· W2998774476 on OpenAlexafffundabout
Hamed Mohammadifardi, Mark A. Knight, Andrè Unger

Bibliographic record

VenuePipelines 2018 · 2018
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSustainabilityAsset (computer security)Sewage treatmentWastewaterBusinessGovernment (linguistics)Asset managementEnvironmental economicsEnvironmental scienceComputer scienceEnvironmental engineeringFinanceEconomics

Abstract

fetched live from OpenAlex

In most Canadian municipalities, wastewater collection (WWC) and wastewater treatment plants (WWTPs) are owned and managed by separate municipal governments. Linear sewer pipe networks are owned and managed by utilities at the lower tier of municipal government, and vertical treatment plant assets are owned and managed by regional governments at the higher level. Under this arrangement, regions charge utilities for treating their collected wastewater. The linkage between the WWC and WWTP systems are presented and modeled by taking the system dynamic approach. Three asset management scenarios are developed and evaluated for a joint WWC and WWTP system. Based on the simulation results, the main goal of financial self-sustainability is achieved when the maximum annual rehabilitation rate is 0.88% of the total WWC network length. However, adapting the higher rehabilitation rate of 1.1% is more desirable because of avoiding the downstream capital costs for building new WWTP capacities. This study demonstrates that the applying such integrated models will help decision makers to evaluate the behavior of interrelated wastewater collection and treatment systems, and find synergistic cost-saving opportunities while taking decisions on when, where, and how to invest in infrastructure upgrading and rehabilitation.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.298

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)0.0000.000

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 head0.019
GPT teacher head0.220
Teacher spread0.202 · 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 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

Citations1
Published2018
Admission routes3
Has abstractyes

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