Targeting R&D quality and compliance training for technology transfer into the GMP environment of production
Bibliographic record
Abstract
Abstract For a long time, veterinary biological vaccine‐producing companies in the United States have been ruled by the quality and compliance requirements imposed by the United States Department of Agriculture (USDA). However, with the globalization of markets, companies now need to comply with quality and compliance guidelines of key foreign markets such as the European Union (EU), Japan and Canada. Since the quality of the final product is built in from inception, i.e. through all development phases, it is imperative that all research and development (R&D) personnel understand the basic global requirements for quality and compliance. This paper presents an innovative approach for designing a training program for R&D personnel in the basic quality and compliance requirements for global product development and technology transfer into production operating under strict good manufacturing practices (GMP) requirements. The training program integrated basic GMP requirements such as sourcing of raw materials, components, qualification of vendors, developing GMP compliant documentation for pilot batch production, setting specifications based on target countries and the proper use of change control. In addition the basics of good laboratory practice (GLP) requirements were incorporated into the training by including a section on the importance of the use of calibrated instrumentation/measuring devices and proper documented approved procedures. Copyright © 2001 John Wiley & Sons, Ltd.
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.
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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".