Random walk with horizontal and cyclic currents
Bibliographic record
Abstract
We construct a minimal two-chain random walk model and study the information that fluctuations of the flux and higher cumulants can reveal about the model: its structure, parameters, and whether it operates under nonequilibrium conditions. The two coupled chains allow for both horizontal and cyclic transport. We capture these processes by deriving the cumulant generating function of the system, which characterizes both horizontal and cyclic transport in the long time limit. First, we show that either the horizontal or the cyclic currents, along with their higher-order cumulants, can be used to unravel the intrinsic structure and parameters of the model. Second, we investigate the "zero current" situation, in which the horizontal current vanishes. We find that fluctuations of the horizontal current reveal the nonequilibrium condition at intermediate bias, while the cyclic current remains nonzero throughout. We also show that in nonequilibrium scenarios close to the zero horizontal current limit, the entropy production rate is more tightly lower-bounded by the relative noise of the cyclic current, and vice versa. Finally, simulations of transport before the steady state sets in allow for the extraction of the interchain hopping rate. Our study, illustrating the information concealed in fluctuations, could see applications in chemical networks, cellular processes, and charge and energy transport materials.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".