Sustainable Supply Chain in the Agri-Food Sector in South-Italy as an Eco-Sustainability Tool for Innovation and Territorial Development
Bibliographic record
Abstract
The agri-food logistics chains face guarantee the greatest extending of sustainability practices in the management of the entire supply chain. The use of management and marketing tools and of innovative legal and economic institutes, also in the agri-food sector, is fundamental to guaranteeing an increasingly “cleaner” agriculture, in order to introduce more and more organic and eco-sustainable food products and safe for the health and well-being of living beings on the market. However, the joint use of these tools does not yet seem to be fully exploited, particularly in South-Italy. In this sense, the territories limit of potential eco-sustainable development tools deriving from the agri-food logistics chains, also in terms of territorial and tourism development of entire areas. Drawing upon the experiences of innovative projects oriented to eco-sustainability that have implemented mechanisms of continuous improvement in the agri-food sector, through the achievement of high levels of technological, methodological and organizational innovation, some implications for how to implement territorial development policies guided by sustainability can be found. In this paper, the changes that the agri-food sector has undergone will be discussed and possible scenarios of territorial and tourism development supported by the agri-food logistics chains will be suggested, two macro areas covered by the three pillars of sustainability, environmental, economic and social.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".