Process validation for manufacturing of biologics and biotechnology products : Berlin Hilton Hotel, Berlin, Germany, 6-7 September, 2001
Bibliographic record
Abstract
Expectations for Process Validation: Canadian Perspectives on Expectations for Process Validation, Ridgway, A. Expectations for Process Validation: Industry Perspectives, Francis, R. Strategies: Evolution of Validation Through the Product Life Cycle Statistical Tools for Setting In-Process Acceptance Criteria, Seely, R.J. Munyakazi, L., Haury, J. experience with Validation of Clinical Trial Materials - An Inspector's Viewpoint, Wolfe, L. Upstream Processes: Validation of Fermentation Processes, Lubiniecki, A.S. et al. Strategies: Evolution of Validation Through the Product Life Cycle Site Transfer and Process Validation in Case of Herceptin API, Kuhne, W. Post-Approval Changes: Case Study Detection and Consequences of Recombinant Protein Isoforms - Implications for Biological Potency, Federici, M.M. et al. Physical Methods of Separation: Current Issues in Validation of Chromatography, Sofer, G. Validation of Sterilizing Grade Filtration, Jornitz, M.W., Meltzer, T.H. Viral Clearance: Viruses and Assuring Viral Safety, Robertson, J.S. Viral Clearance and Inactivation Manufacturing Process for Antithrombin III: Concentrate: Viral Validation Studies and Effects of Column Re-Use on Viral Safety, Falbo, A. An Overview of Quantitative PCR Assays for Biologicals: Quality and Safety Evaluation, Xu,Y. Brorson, K. Re-Processing strategies for Biologicals API Manufacturing Processes, Opitz, U. Re-Processing of Biological Products: Regulatory Considerations from the CBER Perspective, Shacter, E.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.136 | 0.084 |
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".