A review of leveraged sample selection in variation reduction projects
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
Variation reduction in critical-to-quality outputs is often the primary goal in manufacturing process improvement projects. Identifying the cause of output variation is recommended as an intermediate step in finding a low-cost sustainable solution to excessive variation. The goal of this paper is to describe and quantify the benefits of using leveraged sample selection when searching for important causes of output variation. We define leveraged sample selection as choosing parts to investigate that are extreme relative to other parts produced by the manufacturing process. In this paper, we discuss three different types of leveraged sample selection to illustrate the breadth of applicability of leveraging. We also review the existing literature and look at both planning and analysis of investigations that use leveraged sample selection. In addition, we provide a motivating example related to automotive headrests that illustrates the use of each of the three leveraged plans. Using leveraged sample selection in this context constitutes an example of the developing the discipline of Statistical Engineering.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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