Assessing Measurement System Agreement in the Presence of Reproducibility and Repeatability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Assessing the agreement between an established and a new measurement system is a practical and important challenge in many application areas. The probability of agreement (PoA) has recently been introduced as a metric to assess such agreement when repeatability, the precision of the measurement system itself, represents the overall measurement system variation. However, it is common in practice for the measurement system to be operated by multiple individuals, and their effects can be an important part of the overall variation. Reproducibility represents the measurement variability attributable to different operators. This article extends the PoA methodology to account for both the repeatability and reproducibility of each measurement system along with the relative bias between them. The developed methodology also allows imbalanced replicate measurements across operators and systems, while the operator effects can either be fixed or random. We use maximum likelihood estimation to estimate the PoA. The proposed approach is illustrated using two case studies. In the first one, we compare the agreement between an old and a new measurement system used for quality inspections in an industrial context. The second case study which is presented in supplementary materials file assesses the agreement between two devices used to measure respiratory rates.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.209 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it