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Record W4235435413 · doi:10.1002/0471667196.ess7147

Applicability of Statistics and Probability Theory to Nucleate Pool Boiling Heat Transfer

2005· other· en· W4235435413 on OpenAlexaff
R. L. Judd, N. Balakrishnan

Bibliographic record

VenueEncyclopedia of Statistical Sciences · 2005
Typeother
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNucleationBoilingNucleate boilingHeat transferMonte Carlo methodBoiling heat transferPopulationStatistical physicsThermodynamicsMechanicsMaterials scienceMathematicsStatisticsPhysicsHeat transfer coefficientDemography

Abstract

fetched live from OpenAlex

Abstract Much has been learned about nucleate pool boiling over the last sixty years through experimental investigation and recently, it has become possible to predict the relationships governing the nucleation, growth and departure or collapse of bubbles computationally. However, it has not been possible to develop a theory capable of predicting the rate of heat transfer because of the inability to incorporate surface conditions in a comprehensive boiling heat transfer model. A method is proposed to simulate the surface conditions through the interactions of a large population of potentially active nucleation sites and a much smaller population of dominant nucleation sites using Monte Carlo simulation.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.692
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.247
Teacher spread0.237 · 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 designNot applicable
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
Published2005
Admission routes1
Has abstractyes

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