A quality management system application to investigate and troubleshoot process failures
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
Purpose The purpose of this paper is to discuss a practical approach taken by utilizing the non‐conformance/event management and failure investigation (FI) system to formally troubleshoot an actual process failure observed in the sequencing facility. Design/methodology/approach In this study the authors describe how the cause for the poor quality sequence data, as indicated from the quality score, involving high molecular weight follicular lymphoma DNA samples for a study of tumor‐associated genome rearrangements was successfully identified and confirmed through the application of a well structured FI process. Findings Through this FI process the underlying causes were effectively identified, immediate corrective actions were executed and a preventative action to avoid or minimize reoccurrences was also implemented and monitored for effectiveness. Originality/value This paper establishes that by applying a systematic, documented FI process the underlying causes of a process failure in an organization can be effectively identified and appropriate corrective and preventative actions can be successfully adopted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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