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Record W2433887005 · doi:10.2166/hydro.2015.049

An object-oriented development environment to optimally design cyclic storage systems

2015· article· en· W2433887005 on OpenAlexaff
Mohammadamin Jahanpour, Abbas Afshar, Samuel Sandoval-Solís

Bibliographic record

VenueJournal of Hydroinformatics · 2015
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGeneralizationDevelopment (topology)Computer sciencePareto principleMulti-objective optimizationSet (abstract data type)Object (grammar)Mathematical optimizationScale (ratio)Distributed computingMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Cyclic storage system (CSS) is defined as physically interconnected and operationally integrated surface water and groundwater subsystems with full direct interactions between the subsystems. Mathematical development and implementation of a CSS model is very complex and all previous works are fully case dependent with a minimum possibility of generalization. This article proposes an integrated development environment called CSSDev, which assists researchers to create and design object-oriented CSS models more easily. Using CSSDev, researchers may skip regeneration of repetitive simulation codes for common elements of a CSS. CSSDev employs NSGA-II to optimally select the design parameters of the models. Two objective functions of the optimization problem are system's total costs and total loss associated with the development alternatives. A real-world large-scale CSS has been modeled and optimized to illustrate the performance of CSSDev. The final Pareto-front is presented and two selected solutions from the set of optimal non-dominated ones are evaluated and discussed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.640
Threshold uncertainty score0.489

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 communication0.0000.001
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 head0.202
Teacher spread0.182 · 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
GenreMethods

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
Published2015
Admission routes1
Has abstractyes

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