Building Self-Sustainable Basic Food Systems: Role of Bioactive Components and Beyond in Science and Innovation
Bibliographic record
Abstract
The world is actively seeking for ways to establish a global food system that demonstrates sustainability in the realms of food security, food safety, and nutrition security. Reflecting on the profound impacts of the COVID-19 pandemic, ongoing war in Ukraine, and accelerated climate crisis with extreme weather events, there arises an urgent necessity for reevaluating the current approach in building sustainable food systems. This contribution considers opportunities and limitations in moving towards more self-sustainable food systems, particularly for basic foods. It also emphasizes the need for caution when contemplating the pursuit of this endeavor and discusses key issues pertaining to basic foods, deforestation, renewable energies, workforce, supply chains, and the environment. Lastly, the roles of science and innovation within the framework of national self-sustaining basic foods systems are elucidated, including opportunities in optimizing the utilization of food bioactive components. It is anticipated that the framework can serve as a tool to foster the development of comprehensive policies that suits the particular needs and development stage of each country. These policies, in turn, will advance the implementation of technologies, promote culture cultivation, and facilitate education and training, all geared towards achieving the goals of a more resilient and sustainable food system.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".