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Record W4230174398 · doi:10.1017/cbo9781139171267.013

Selected diffusion data

2000· other· en· W4230174398 on OpenAlexaff
David S. Wilkinson

Bibliographic record

Venuenot available
Typeother
Languageen
FieldComputer Science
TopicAdvanced Mathematical Modeling in Engineering
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMass transportField (mathematics)Mass transferTransport phenomenaGraduate studentsStatistical physicsComputer scienceEngineering physicsTheoretical physicsEngineeringMechanical engineeringPhysicsThermodynamicsMathematics

Abstract

fetched live from OpenAlex

The field of matter transport is central to understanding the processing of materials and their subsequent mechanical properties. While thermodynamics determines the final state of a material system, it is the kinetics of mass transport that governs how it gets there. This book, first published in 2000, gives a solid grounding in the principles of matter transport and their application to a range of engineering problems. The author develops a unified treatment of mass transport applicable to both solids and liquids. Traditionally matter transport in fluids is considered as an extension of heat transfer and can appear to have little relationship to diffusion in solids. This unified approach clearly makes the connection between these important fields. This book is aimed at advanced undergraduate and beginning graduate students of materials science and engineering and related disciplines. It contains numerous worked examples and unsolved problems. The material can be covered in a one semester course.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.231
Threshold uncertainty score0.771

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2310.144

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 head0.023
GPT teacher head0.257
Teacher spread0.234 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2000
Admission routes1
Has abstractyes

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