Cooking up Diverse Diets: Advancing Biodiversity in Food and Agriculture through Collaborations with Chefs
Bibliographic record
Abstract
ABSTRACT Biodiversity in and across food and agriculture systems provides tremendous value to present and future generations. However, across the world we are losing genes, species, and ecosystems faster than we can account for them. With one million plant and animal species at risk of extinction, our society is challenged to address the drivers of ecosystem degradation and species loss. Increasingly, the negative impacts of agriculture and food systems on biodiversity are being raised as well as the global risks to health associated with unhealthy diets. Recent efforts in North America to raise awareness of crop diversity and coordinate plant conservation efforts culminated in a symposium with botanic gardens, agricultural researchers, wild land managers, conservation organizations, academics, and government bodies. The gathering focused primarily on production‐side solutions such as crop diversity, crop wild relative conservation, and agricultural education. Although not present at the symposium, chefs were commonly highlighted and discussed as key collaborators in plant conservation through their important role in connecting consumers to agriculture production and new food plants. This paper shares examples of chefs and culinary programs working to impact agriculture, diets, and plant diversity. To critically assess which chef and culinary programs are having the greatest impact future researched is needed, but as we race to save plant species from extinction, it is clear that chefs connect to consumers in unique ways and are important potential allies in cooking up new sustainable consumption and production patterns that support biodiversity in food systems.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 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".