Kinetic Capillary Electrophoresis (KCE): A Conceptual Platform for Kinetic Homogeneous Affinity Methods
Bibliographic record
Abstract
We propose kinetic capillary electrophoresis (KCE) as a conceptual platform for the development of kinetic homogeneous affinity methods. KCE is defined as the CE separation of species that interact during electrophoresis. Depending on how the interaction is arranged, different KCE methods can be designed. All KCE methods are described by the same mathematics: the same system of partial differential equations with only initial and boundary conditions being different. Every qualitatively unique set of initial and boundary conditions defines a unique KCE method. Here, we (i) present the theoretical bases of KCE, (ii) define four new KCE methods, and (iii) propose a multimethod KCE toolbox as an integrated kinetic technique. Using the KCE toolbox, we were able to, for the first time, observe high-affinity (specific) and low-affinity (nonspecific) interactions within the same protein-ligand pair. The concept of KCE allows for the creation of an expanding toolset of powerful kinetic homogeneous affinity methods, which will find their applications in studies of biomolecular interactions, quantitative analyses, and selecting affinity probes and drug candidates from complex mixtures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".