Introducing Quantitative Assessment of Michaelis Constant ( <i>K</i> <sub>m</sub> ) Accuracy
Bibliographic record
Abstract
The Michaelis constant ( K m ) is central to enzyme kinetics, guiding variant selection, inhibitor screening, and metabolic modeling. However, K m obtained by nonlinear regression can be substantially inaccurate even when the reported standard error (SE) appears small. Common software reports SE but provides no accuracy metric. This gap is addressed by extending the accuracy confidence interval (ACI) framework to K m (ACI‐ K m ) through a binding‐isotherm formulation of the velocity–substrate fit. Given confidence intervals for concentration accuracy, the method quantifies how residual systematic uncertainties in enzyme and substrate concentrations ( E 0 and S 0 ) propagate into the determined K m values and provides a probabilistic interval expected to enclose the accurate value. The approach requires no additional kinetic experiments and is directly applicable to existing datasets. Concentration‐accuracy intervals can be estimated from calibration data, reagent specifications, or quality‐control records. ACI‐ K m is valid across a wide range of E 0 / K m conditions, including relatively high E 0 . A free web application ( https://aci.sci.yorku.ca ) implements ACI‐ K m . Tests on synthetic and experimental datasets show that K m ± SE can severely underestimate uncertainty, whereas ACI provides more reliable accuracy bounds for decision‐making, complementing rather than replacing traditional precision metrics by providing quantitative diagnostic bounds for concentration‐related uncertainties in K m determination.
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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 | 0.018 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".