Traceability, Liability and Incentives for Food Safety and Quality
Bibliographic record
Abstract
may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. 1Traceability, Liability and Incentives for Food Safety and Quality Recent food safety concerns and well-publicized food scares have heightened awareness of traceability in the food supply chain. When the first U.S. case of Bovine Spongiform Encephalopathy (BSE or “mad cow disease”) was discovered in Washington State, federal authorities suggested that “it might take weeks, even months, to track the origins of the diseased cow ” (Clemetson and Simon, p.1). With the cooperation of herd owners, livestock dealers and market operators as well as detailed record searches between United States and Canadian agencies, the authorities were able to trace the origin of the affected cow to Canada only after a week, but herd mates were never fully traced. The December 2003 case of BSE in Washington State highlighted the demand for traceability to regain consumer confidence after the discovery of a first event. In addition, in the case of highly contagious disease or when multiple related dangers are suspected, traceability is important to reduce risk of further damage.
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 | 0.032 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 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".