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Record W4253256567 · doi:10.1017/9781108526227.020

Elementary Primer on Partial Differential Equations (PDEs)

2018· other· en· W4253256567 on OpenAlexaff
William Hoiles, Vikram Krishnamurthy, Bruce Cornell

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMathematics
TopicNumerical methods for differential equations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPartial differential equationApplied mathematicsMathematicsUniquenessScope (computer science)Calculus (dental)Computer scienceMathematical analysis

Abstract

fetched live from OpenAlex

Chapters 10–12 described continuum models of engineered membranes that involved partial differential equations (PDEs). This appendix gives a brief introduction to the classification of PDEs, linear PDEs, nondimensionalization of PDEs, and numerical methods for solving PDEs. Uniqueness and existence results are not discussed here since they involve advanced results in functional analysis that are outside the scope of this book. For a comprehensive advanced treatment of PDEs at a graduate mathematics level see [115].

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.430
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.1830.005

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 head0.095
GPT teacher head0.387
Teacher spread0.292 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2018
Admission routes1
Has abstractyes

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Same topicNumerical methods for differential equationsFrench-language works237,207