Bibliographic record
Abstract
Chemical studies usually consist of measurements made on large ensembles of molecules with data representing average values for the population. It has been shown that individual molecules of a given enzyme have different properties. Large-scale averaging has in the past masked these differences. Alkaline phosphatase has been used as a model to study this enzyme heterogeneity. The catalytic rates of the individual molecules have been found to differ by over 10-fold, and the activation energy of catalysis by more than two-fold. Differences in properties indicate that differences in structure must exist between the molecules. For alkaline phosphatase, the structural differences have been suggested to be differences in glycosylation, differences due to partial proteolysis, and due to some molecules containing mixtures of active and inactive subunits. The determination of the distribution of activities of populations of this enzyme within a sample has also been shown to be a useful tool in diagnostics. This review discusses the advent of single-molecule enzymology and summarizes its use in the study of alkaline phosphatase using capillary electrophoresis, microscopic well assays, and single-molecule tracking.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".