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Record W4235635581 · doi:10.1002/9781119517566.ch16

Differentiation and Integration on Smooth Manifolds

2019· other· en· W4235635581 on OpenAlexaff
Stephen C. Newman

Bibliographic record

Venuenot available
Typeother
Languageen
FieldComputer Science
TopicAdvanced Mathematical Modeling in Engineering
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAtlas (anatomy)Vector fieldDiffeomorphismCurl (programming language)Manifold (fluid mechanics)MathematicsPure mathematicsVolume formMathematical analysisDivergence (linguistics)Differential formGeometryGeologyComputer scienceScalar curvatureHermitian manifoldPaleontologyCurvature

Abstract

fetched live from OpenAlex

This chapter defines two differential operators on smooth manifolds—the exterior derivative and the Lie derivative. Despite their evident differences in construction and properties, they are related by a remarkable identity that involves interior multiplication. The chapter then presents three examples to show that the exterior derivative is related to the classical curl, gradient, and divergence operators. It shoes that a smooth manifold is orientable if it has a consistent smooth atlas. Evidently, the concept of an orientation of a smooth manifold and that of a consistent smooth atlas for a smooth manifold are closely related, almost to the extent of being indistinguishable. The chapter also introduces the theory of integration on smooth manifolds. It shows that the maximal integral curves corresponding to a vector field combine to form a smooth map defined on an open set, and that the “flow” of particles referred to above takes place in a “diffeomorphic” fashion.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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 designTheoretical or conceptual
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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