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Temperature Fluctuations and Mixtures of Equilibrium States in the Canonical Ensemble

2004· book-chapter· en· W1680744099 on OpenAlex

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affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOxford University Press eBooks · 2004
Typebook-chapter
Languageen
FieldPhysics and Astronomy
TopicStatistical Mechanics and Entropy
Canadian institutionsMcGill University
Fundersnot available
KeywordsInverse temperatureThermodynamicsStatistical mechanicsCanonical ensembleStatistical physicsObservablePhysicsExponential functionNegative temperatureInverseQuantum mechanicsMathematicsMonte Carlo methodStatistics

Abstract

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It has been suggested recently that "Q-exponential" distributions, which form the basis of Tsallis' nonextensive thermostatistical formalism, may be viewed as mixtures of exponential (Gibbs) distributions characterized by a fluctuating inverse temperature. In this chapter, we revisit this idea in connection with a detailed microscopic calculation of the energy and temperature fluctuations present in a finite vessel of perfect gas thermally coupled to a heat bath. We find that the probability density related to the inverse temperature of the gas has a form similar to a x<sup>2</sup> density, and that the "mixed" Gibbs distribution inferred from this density is non-Gibbsian. These findings are compared with those obtained by a number of researchers who worked on mixtures of Gibbsian distributions in the context of velocity difference measurements in turbulent fluids as well as secondary distributions in nuclear scattering experiments…. Most, if not all, textbooks on thermodynamics and statistical physics define temperature as being a quantity which, contrary to other thermodynamic observables like energy or pressure, does not admit fluctuations. Because of that, it is somewhat surprising to see papers with the expression "temperature fluctuations" in their titles appearing from time to time in serious scientific journals on subjects as various as particle physics and fluid dynamics (see, e.g., Ashkenazi and Steinberg [3], Ching [9], Chiu et al. [10], and Stodolsky [24]). Indeed, how can the temperature of a system, however small, fluctuate if one defines it "as equal to the temperature of a very large heat reservoir with which the system is in equilibrium and in thermal contact" [18]? Also, in the case of the reservoir, how can temperature be a fluctuating parameter if its definition requires one to assume the thermodynamic limit, in other words, to assume that the system acting as a reservoir is composed of an infinite number of particles or degrees of freedom? Presumably, the thermodynamic limit should rule out any fluctuations of thermodynamic quantities like the mean energy or the pressure, so that if temperature is related to these quantities, how can it fluctuate?

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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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.656
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.209
Teacher spread0.197 · 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