European pork chains : diversity and quality challenges in consumer-oriented production and distribution
Bibliographic record
Abstract
In this book the results are presented of a comprehensive inventory of pork chains that has been conducted through expert interviews and in-depth case studies. The main focus of the book is on how well diverse and fragmented supply in the European pork sector matches differentiating demands for pork products in rapidly evolving markets. One of the central topics discussed in the book is management of quality in diverse mainstream and specialty European pork chains. Inter-enterprise information systems, governance forms, logistics and sustainability aspects of European pork chains are also presented, as well as a number of interesting innovations in the chains. 'European pork chains' consists of four chapters that discuss the European pork chain as a whole and nine chapters that present case studies. The latter comprise three specialty pork chains (Iberian ham from Spain, Mangalica pork from Hungary, and organic pork from the Netherlands) and three regional pork chains in Europe (a Greek integrated chain, the German 'Eichenhof' chain and the French 'Cochon de Bretagne' chain). To enable comparison with chains outside Europe, a review of pork chains in China, Canada, Brazil and South Africa has been included.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".