Bibliographic record
Abstract
Lapses in food safety have spurred development of governmental traceability systems to track every stage of food production as part of a standardized information base. These systems form part of national and international government efforts to reduce food‐security risks and control food‐related disease outbreaks. The European Union, the United States, Japan, and Canada have traceability requirements now in various stages of implementation, as does the Codex Alimentarius. Traceability regulations require that, from farm (plant or animal) to fork, foods have a clear, verifiable record that tracks through all stages of cultivation, production, supplying, transporting, processing, and distribution. Traceability implies complete information control over the geography of one of life's most essential acts, eating. The apparent object of traceability is food, which seems to imply that human tracking is not part of the process, but food does not move on its own. Those people responsible at each stage for food transfers and transactions may go into the traceability database, making their locations part of the record and supporting precise monitoring of labor performance, consumer buying patterns, and ownership and management strategies. Given these capabilities, the development of public‐sector traceability systems demands careful consideration. Owners, especially large exporters and importers, are likely to see their needs and fears shape the system. The food workforce may well bear tracking's brunt. Consumers, the presumed beneficiaries of the systems, will probably resist direct incorporation (and full benefit), favoring their privacy over their safety.
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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.016 | 0.037 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".