MétaCan
Menu
Back to cohort
Record W2142525684 · doi:10.1080/01919510108962014

Development of Transient Back Flow Cell Model (BFCM) for Bubble Columns

2001· article· en· W2142525684 on OpenAlexaff
Mohamed Gamal El‐Din, Daniel Smith

Bibliographic record

VenueOzone Science and Engineering · 2001
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransient (computer programming)Ordinary differential equationFlow (mathematics)MathematicsDifferential equationBubbleTransient responseAlgebraic equationAlgebraic numberSimple (philosophy)Applied mathematicsMechanicsMathematical analysisPhysicsNonlinear systemComputer scienceGeometryEngineering

Abstract

fetched live from OpenAlex

A Transient Back Flow Cell Model (BFCM) is presented as an alternative approach to describe the hydrodynamics of ozone bubble columns. Transient BFCM, when compared to the traditionally used models such as transient ADM or transient CFSTR's in-series model, represents a flexible, reliable, accurate, and more importantly simple method to describe the backmixing in the liquid phase in bubble columns. Transient BFCM consists of NBFCM ordinary first-order differential equations in which NBFCM unknowns (Yj's) are to be determined. This set of NBFCM differential equations can be solved numerically as NBFCM linear algebraic equations with respect to rime as the independent variable. This is achieved by applying an explicit technique for the discrete time which allows obtaining the solutions (Yj n 's)of the NBFCM algebraic equations simultaneously at time step “n+1” as functions of the (Yj n+1 's)values at time step “n”.

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)0.000
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.182
Teacher spread0.172 · 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

Citations8
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueOzone Science and EngineeringSame topicFluid Dynamics and MixingFrench-language works237,207