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Record W2126698612 · doi:10.1103/physreve.75.011133

Jarzynski equality: Connections to thermodynamics and the second law

2007· article· en· W2126698612 on OpenAlexaff
Benoit Palmieri, David Ronis

Bibliographic record

VenuePhysical Review E · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Thermodynamics and Statistical Mechanics
Canadian institutionsMcGill University
Fundersnot available
KeywordsNon-equilibrium thermodynamicsDissipative systemSecond law of thermodynamicsWork (physics)Upper and lower boundsFluctuation theoremStatistical physicsFunction (biology)Laws of thermodynamicsPhysicsThermodynamicsThermodynamic free energyMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

The one-dimensional expanding ideal gas model is used to compute the exact nonequilibrium distribution function. The state of the system during the expansion is defined in terms of local thermodynamics quantities. The final equilibrium free energy, obtained a long time after the expansion, is compared against the free energy that appears in the Jarzynski equality. Within this model, where the Jarzynski equality holds rigorously, the free energy change that appears in the equality does not equal the actual free energy change of the system at any time of the process. More generally, the work bound that is obtained from the Jarzynski equality is an upper bound to the upper bound that is obtained from the first and second laws of thermodynamics. The cancellation of the dissipative (nonequilibrium) terms that result in the Jarzynski equality is shown in the framework of response theory. This is used to show that the intuitive assumption that the Jarzynski work bound becomes equal to the average work done when the system evolves quasistatically is incorrect under some conditions.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.006
Scholarly communication0.0030.006
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.322
Teacher spread0.308 · 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

Citations23
Published2007
Admission routes1
Has abstractyes

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