The next era of crop domestication starts now
Bibliographic record
Abstract
Current food systems are challenged by relying on a few input-intensive, staple crops. The prioritization of yield and the loss of diversity during the recent history of domestication has created contemporary crops and cropping systems that are ecologically unsustainable, vulnerable to climate change, nutrient poor, and socially inequitable. For decades, scientists have proposed diversity as a solution to address these challenges to global food security. Here, we outline the possibilities for a new era of crop domestication, focused on broadening the palette of crop diversity, that engages and benefits the three elements of domestication: crops, ecosystems, and humans. We explore how the suite of tools and technologies at hand can be applied to renew diversity in existing crops, improve underutilized crops, and domesticate new crops to bolster genetic, agroecosystem, and food system diversity. Implementing the new era of domestication requires that researchers, funders, and policymakers boldly invest in basic and translational research. Humans need more diverse food systems in the Anthropocene-the process of domestication can help build them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".