Experimental evidence of detailed balance in granular systems
Bibliographic record
Abstract
Abstract The principle of detailed balance (DB) states that every kinetic transition in a system with many micro-states, $$\mu $$ , is balanced, on average, with the opposite transition, $$\mu _i\leftrightharpoons \mu _j$$ . The current perception is that, on the scale of the most elementary degrees of freedom, DB is satisfied only in equilibrium systems, although a rigorous proof exists only for thermal systems. It is believed that, on this scale, non-equilibrium steady states can only be balanced by cycles, such as $$A\rightarrow B\rightarrow C\rightarrow A$$ . We report here experiments on a family of out-of-equilibrium quasi-statically cyclically sheared granular systems, which appear to show robust DB. We then analyse in detail the concept and interpretation of DB and show that our systems are the exact equivalent of chemically reactive systems in thermal equilibrium. We therefore conclude that our non-equilibrium systems do indeed satisfy this principle. We further study the approach to DB as a function of system size and time. Given the significant progress to which this principle has led in equilibrium systems, these observations may pave the way for better models of the dynamics and statistical mechanics of these and potentially other non-equilibrium systems.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".