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Record W2900649452 · doi:10.1090/conm/720/14522

From classical mechanics to quantum mechanics

2018· other· en· W2900649452 on OpenAlexaffabout
Richard Froese

Bibliographic record

VenueContemporary mathematics - American Mathematical Society · 2018
Typeother
Languageen
FieldPhysics and Astronomy
TopicQuantum Mechanics and Applications
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMathematicsAnalytical dynamicsGeometric mechanicsQuantum statistical mechanicsSupersymmetric quantum mechanicsClassical mechanicsQuantum mechanicsAnalytical mechanicsQuantumPhysics

Abstract

fetched live from OpenAlex

These are the notes for five one hour lectures delivered at the 2016 CRM summer school in Spectral Theory at Laval University. They were intended to provide some general background on classical and quantum mechanics to advanced undergraduate and beginning graduate students in mathematics. The first four lectures contain an outline of these two theories from a mathematical point view and a comparison of the classical and quantum descriptions of some simple systems. The last lecture is about hidden variables and is meant to illustrate the essential strangeness of the quantum description of nature. I have tried to make these notes more accessible by including an outline of some basic ideas in measure theory and operator theory. A similar outline of the basics of manifolds, tangent and cotangent spaces and differential forms would have been useful for understanding the sections on Lagrangian submanifolds and Hamilton-Jacobi equations. Unfortunately I did not have time to include this. I am grateful to Cyrus Bhiladvala and the referee for making suggestions for improvement.

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)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.063
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0630.007

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 head0.026
GPT teacher head0.282
Teacher spread0.256 · 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
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 routes2
Has abstractyes

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