MASKE: Macroscopic Approach to Studying Kinetics at Equilibrium
Bibliographic record
Abstract
The kinetics of biomolecular interactions at equilibrium is typically studied by "microscopic" methods that monitor concentration fluctuations of molecules in an "observation" volume in which the number of molecules is so small that the equilibrium is statistically impossible. Here, we introduce a "macroscopic" method for studying kinetics of biomolecular interactions at equilibrium which does not rely on monitoring the fluctuation of concentrations. We termed this method MASKE: a "macroscopic approach to studying kinetics at equilibrium". Conceptually, in MASKE, two equilibrium reaction mixtures, "unlabeled" and "labeled", are both prepared with two reactants and their complex; in the labeled mixture, one reactant is labeled for detection. A "macroscopic" amount of the labeled mixture is introduced into a long and narrow reactor filled with the unlabeled mixture, and a differential mobility of the reactant versus the complex is then induced by an external action along the reactor. The kinetics of complex formation and dissociation is then studied from the label-propagation pattern. In this work, we developed the theory of MASKE and experimentally proved it with a capillary as a reactor, a fluorophore as a label, and an electric field as a differential mobility inducer. Two pairs of molecules interacting with significantly different rate constants were used in this proof-of-principle work.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".