Machine Learning in Measurement Part 2: Uncertainty Quantification
Bibliographic record
Abstract
In spite of the advent of Machine Learning (ML) and its successful deployment in measurement systems, little information can be found in the literature about uncertainty quantification in these systems [1]. Uncertainty is crucial for the adoption of ML in commercial products and services. Designers are now being encouraged to be upfront about the uncertainty in their ML systems, because products that specify their uncertainty can have a significant competitive advantage and can unlock new value, reduce risk, and improve usability [2]. In this article, we will describe uncertainty quantification in ML. Because there isn't enough room in one article to explain all ML methods, we concentrate on Deep Learning (DL), which is one of the most popular and effective ML methods in I&M [3]. Please note that this article follows and uses concepts from Part 1 [4], so readers are highly encouraged to first read that part. In addition, we assume the reader has a basic understanding of both DL and uncertainty. Readers for whom this assumption is false are encouraged to first read the brief introduction to DL and its applications in I&M presented in [3] as well as the uncertainty tutorial in [5].
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".