General Approach to High-Efficiency Simulation of Affinity Capillary Electrophoresis
Bibliographic record
Abstract
The differential equation describing electrophoretic migration can be evaluated with various finite difference schemes (FDSs). However, the accuracy and efficiency can be dramatically different depending on the FDS chosen and the way the algorithm is implemented in a computer simulation program. The monotonic transport scheme is used as the algorithm for the hyperbolic part of the differential equation, and the first-order fully explicit scheme is used for the parabolic part of the equation. The combination of these algorithms minimizes the errors and maintains high efficiency. A circular arrangement of the cells in the computer's memory is used in the implementation of the algorithms, and the use of concentration thresholds to enable and disable cells along the capillary makes the new algorithm highly efficient. Either thermodynamic or kinetic constants can be used in this program to simulate binding interactions between two species for equilibrium and nonequilibrium affinity CE. Simulation results with various parameters are presented. The simulated peak with proper parameters for an equilibrium affinity CE experiment has shape and position similar to that of the experimental peak. The simulated electropherograms for a nonequilibrium affinity CE experiment also show characteristics of the experimental electropherograms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".