Review on Data Integrity Based on The Principle of ALCOA
Bibliographic record
Abstract
Data integrity is the cornerstone of 21 CFR Part 11, which was published in the United States, and is essential for enforcing rules. FDA officials wished to guarantee that accurate data was collected by the pharmaceutical industry along the course of the medication’s lifespan. Even if this information becomes one of the most valuable assets of any company, if they are not honest, they are not very useful to prevent data from being altered, copied, or transferred, it also entails using appropriate documentation practices. Regarding data integrity, all original records— whether stored electronically or on paper—including source data and metadata are referred to as data. Several regulatory agencies, such as the USFDA, Health Canada, and EMEA, recommended ALCOA to guarantee the accuracy of the data (Attributable, Legible, Contemporaneous, Original, and Accurate.
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 | 0.058 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".