SIMULATION RELIABILITY OF PHYSIOLOGICAL PHENOMENA BY CARDIAC ACTION POTENTIAL MODEL
Bibliographic record
Abstract
The single channel recordings not only help understand how a single cell responds to environments but also make the simulation more and more accurate to pinpoint the mechanisms that are not easily found by experiments. In cardiac arrhythmic studies, dynamic action potential models presented by Luo and Rudy are very popular in recent decades. Nevertheless, the simulation results could generate unwanted mistake explanation if the algorithms were not selected carefully. Also, the computing capability of personal computer becomes so powerful to run the cardiac simulations at personal lab or home. To simulate an action potential by L‐Rd model, high order hybrid algorithms, called RK4‐Hybrid, can be 100 times faster than RK4; however, it is rare to mention about the accuracy or reliability of such a simulation on the physiological phenomena. We have found that high order hybrid algorithm, RK4‐hybrid of voltage step (0.05–0.2) could cause fake simulation that always shows R:S (1:2), despite that RK4‐hybrid of voltage step (0.2–0.8) can correctly simulate physiologic phenomena. In this study, we investigate the simulation reliability and accuracy of high order hybrid algorithms in comparison with RK4, so that a suggestion can be given how to select the right algorithm to simulate the physiological phenomena with computing speed fast enough saving huge amount of the computing time in the cardiac simulations.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 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".