Bibliographic record
Abstract
Fox and colleagues (Fox, 1997; Fox ang Lu, 1994) derived an algorithm based on stochastic differential equations for approximating the kinetics of ion channel gating that is substantially simpler and faster than "exact" algorithms for simulating Markov process models of channel gating. However, Mino and colleagues (2002) argued that the approximation may not be sufficiently accurate in describing the statistics of action potential generation. Bruce (2007) subsequently showed that some of the inaccuracies described by Mino et al. (2002 were due to implementation choices, but several important inaccuracies remained. The objective of this study was to develop a framework for analyzing the remaining inaccuracies and determining their origin. Simulations of a patch of membrane with voltage-gated sodium and potassium channels were performed using an exact algorithm for the kinetics of channel gating and the approximate algorithm of Fox. The Fox algorithm assumes that channel gating particle dynamics have a stochastic term that is uncorrelated, zero-mean Gaussian noise, whereas the simulation results of this study demonstrate that in many cases the stochastic term in the Fox algorithm should be correlated and non-Gaussian noise with a non-zero mean. The results indicate that the source of these differences in noise statistics is that the Fox algorithm does not adequately describe the combined behavior of the multiple activation particles in each sodium and potassium channel (three and four, respectively)
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".