Nonequilibrium Capillary Electrophoresis of Equilibrium Mixtures, Mathematical Model
Bibliographic record
Abstract
We recently introduced a new electrophoretic method, nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM). NECEEM provides a unique way of finding kinetic and equilibrium parameters of the formation of intermolecular complexes from a single electropherogram and allows for the use of weak affinity probes in protein quantitation. In this work, we study theoretical bases of NECEEM by developing a mathematical model for the new method. By solving a system of partial differential equations with diffusion in linear approximation, we found the analytical solution for concentrations of components involved in complex formation as functions of time from the beginning of separation and position in the capillary. The nonnumerical nature of the solution makes it a powerful tool in studying the theoretical foundations of the NECEEM method and modeling experimental results. We demonstrate the use of the model for finding binding parameters of complex formation by nonlinear regression of NECEEM electropherograms obtained experimentally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".