Baseline Practices for the Application of Genomic Data Supporting Regulatory Food Safety
Bibliographic record
Abstract
The application of new data streams generated from next-generation sequencing (NGS) has been demonstrated for food microbiology, pathogen identification, and illness outbreak detection. The establishment of best practices for data integrity, reproducibility, and traceability will ensure reliable, auditable, and transparent processes underlying food microbiology risk management decisions. We outline general principles to guide the use of NGS data in support of microbiological food safety. Regulatory authorities across intra- and international jurisdictions can leverage this effort to promote the reliability, consistency, and transparency of processes used in the derivation of genomic information for regulatory food safety purposes, and to facilitate interactions and the transfer of information in the interest of public health.
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 | 0.338 | 0.257 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".