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Record W2964932413 · doi:10.3390/jmse7080246

Multigene Genetic-Programming-Based Models for Initial Dilution of Laterally Confined Vertical Buoyant Jets

2019· article· en· W2964932413 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Marine Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsGenetic programmingGeneralizationDilutionGenetic algorithmJet (fluid)MathematicsComputer scienceApplied mathematicsMechanicsAlgorithmPhysicsStatistical physicsMathematical optimizationArtificial intelligenceMathematical analysisThermodynamics

Abstract

fetched live from OpenAlex

A new approach based on the multigene genetic-programming (MGGP) technique is proposed to predict initial dilution of vertical buoyant jets subjected to lateral confinement. The models are trained and tested using experimental data, and the good matches demonstrate the generalization and predictive capabilities of the evolved MGGP-based models. The best Pareto-optimal MGGP-based model is also compared with the model evolved using a single-gene genetic-programming (SGGP) algorithm and an existing regression-based empirical equation. The comparisons reveal the superiority of the MGGP-based model. This study confirms that the MGGP technique is promising in evolving an explicit, accurate, and compact model, and the developed models can be employed to estimate effectively and efficiently the dilution properties of a laterally confined vertical buoyant jet.

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.

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 score0.180
Threshold uncertainty score0.377

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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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