Stochastic thermodynamics: experiment and theory
Bibliographic record
Abstract
Stochastic thermodynamics describes the non-equilibrium behavior of mesoscopic physical systems and has emerged as a well-defined subfield of statistical physics during the last few decades. Nowadays, there exists a vibrant community of statistical physicists working in stochastic thermodynamics. While much of the initial progress in this field was theoretical or focused on thought experiments such as the celebrated Maxwell demon, impressive technological advances in recent years have enabled tests of many of the fundamental principles.The workshop Stochastic Thermodynamics: Experiment and Theory, held at the Max-Planck Institute for Complex Systems in Dresden, 10–14 September 2018, had as a primary goal to bring together theorists and experimentalists to discuss the state of the art stochastic thermodynamics and the main future challenges. The workshop was characterized by a vibrant atmosphere, with participants from all over the world sharing their views on the latest results and the outstanding open questions in this field. Many of these discussions have resulted in novel collaborations and significant steps forward.This special edition of Journal of Statistical Mechanics: Theory and Experiment collects the outcome of these discussions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".