Blockchains effects on responsiveness to recalls in the food and beverage industry
Bibliographic record
Abstract
Blockchain technology, by revolutionizing the way businesses use data, is shifting the cost-responsiveness frontier. While the most popular application of blockchain is cryptocurrency, nowadays it is touching many other businesses including the food and beverage industry. This paper is a short survey in assessing the usefulness of blockchain technology in the food and beverage supply chain, with a narrow focus on the impact on the product recalls. While recalls are crucial in the food and beverage industry, as they deal with public health, they happen frequently and therefore an efficient and responsive recall process is essential. This paper investigates whether US companies utilizing blockchain technology experience shorter recall durations. Data from Food and Drug Administration (FDA) recall datasets, specifically targeting companies implementing blockchain technology, are analyzed using statistical analysis methods. The results reveal that companies adopting blockchain technology have significantly shorter recall times, demonstrating their usefulness in food and beverage recalls, along with its other advantages. This study highlights the potential of blockchain in improving recall management within the food and drink industry and provides applicable insights for food and beverage supply chain managers.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".