Review and Performance Evaluation of Uncertainty Quantification in Data-Driven AI-Assisted Measurements
Bibliographic record
Abstract
As Artificial Intelligence (AI) becomes more prevalent in measurement systems and synthetic instruments, quantifying the uncertainty of AI-assisted measurements becomes a crucial and necessary part of the measurement process. In this paper, we take a holistic approach towards both measurement science and AI’s formulation and implementation of uncertainty, and we review and categorize data-driven AI-assisted uncertainty quantification methods with a novel taxonomy. We also provide a one-stop shop identifying AI literature practices that are noncompliant with measurement standards, allowing readers to spot such noncompliances and understand their practical impact. Furthermore, specifically for classification-assisted measurements, we test the most common epistemic Type A uncertainty quantification methods with 12 diverse datasets, and we evaluate their indirect measurement accuracy, one of the most important metrics for engineering applications, as well as precision, recall, and F1 score, each in both macro and weighted modes. Finally, we study the multi-observation variation of misclassification probability and experimentally show that in some cases it can be an indication of uncertainty: an interesting fact considering misclassification probability itself is not uncertainty.
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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 | 0.024 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".