Robustness and Efficiency Considerations When Testing Process Reliability with a Limit of Detection
Bibliographic record
Abstract
Processes in biotechnology are considered reliable if they produce samples satisfying regulatory benchmarks. For example, laboratories may be required to show that levels of an undesirable analyte rarely (e.g., in less than 5% of samples) exceed a tolerance threshold. This can be challenging when measurement systems feature a lower limit of detection, rendering some observations left-censored. We investigate the implications of detection limits on location-scale model-based inference in reliability studies, including their impact on large and finite sample properties of various estimators and the sensitivity of results to model misspecification. To address the need for robust methods, we introduce a flexible weakly parametric model in which the right tail of the response distribution is approximated using a piecewise-constant hazard model. Simulation studies are reported that investigate the performance of the established and proposed methods, and an illustrative application is given to a study of drinking can weights. We conclude with a discussion of areas warranting future work.
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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 | 0.122 | 0.490 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".