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Record W2094538151 · doi:10.1142/s0217732303007965

NEW MINIMUM UNCERTAINTY STATES FOR THERMO FIELD DYNAMICS

2003· article· en· W2094538151 on OpenAlexfundno aff
Hong-Yi Fan

Bibliographic record

VenueModern Physics Letters A · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaChinese Academy of SciencesUniversity of Alberta
KeywordsPhysicsField (mathematics)Dynamics (music)Space (punctuation)Operator (biology)State (computer science)Relation (database)PolynomialHermite polynomialsState spaceThermal quantum field theoryStatistical physicsVariable (mathematics)Field theory (psychology)Classical mechanicsTheoretical physicsApplied mathematicsMathematical physicsMathematical analysisQuantum mechanicsMathematicsPure mathematicsQuantum gravityAlgorithmComputer science

Abstract

fetched live from OpenAlex

In Takahashi–Umezawa Thermo Field Dynamics theory every operator a acting on real field space is accompanied by its image ã acting on fictitious field space, we propose a new uncertainty relation which embodies the finite temperature effect and derive the corresponding minimum uncertainty state — a new thermolized two-variable Hermite polynomial state.

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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.235
Teacher spread0.224 · 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
GenreEmpirical

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

Citations4
Published2003
Admission routes1
Has abstractyes

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Same venueModern Physics Letters ASame topicModel Reduction and Neural NetworksFrench-language works237,207