IMPLEMENTATION OF THE GS1 GLOBAL TRACEABILITY STANDARD BY MARKET OPERATORS FOR THE PRODUCTION OF FLOUR CONFECTIONERY PRODUCTS USED USE OF CAROB POWDER
Bibliographic record
Abstract
Today, the need of introduction of a traceability system in Ukraine is being actively discussed in both the private and public sectors.On the one hand, this is required by national and international law, on the other hand, many years of experience of food companies in the EU, USA, Canada, Japan and other developed countries have proven the effectiveness of a well-established traceability system as a tool to protect business and consumers.It is noted that the main goal of traceability is to respond quickly and find the source of the problem related to food safety, and to take all necessary measures to recall/withdraw from circulation a certain food product with minimal interference in the production process.The company must have a traceability system that allows to identify consignments of food and their relationship to consignments of raw materials, packaging that is in direct contact with the product, packaging that is intended or expected to be in direct contact with food.The traceability system should include all records related to the process of making and distribution of products.Traceability must be ensured and documented prior to the moment of delivering to the customer.The GS1 Global Traceability Standard has been found to include: identification of participants and trading partners, trade items and events; marking and/or methods
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".