Building a feral future: Open questions in crop ferality
Bibliographic record
Abstract
Societal Impact Statement Given the rapidly increasing drought and temperature stresses associated with climate change, innovative approaches for food security are imperative. One understudied opportunity is using feral crops—plants that have escaped and persisted without cultivation—as a source of genetic diversity, which could build resilience in domesticated conspecifics. In some cases, however, feral plants vigorously compete with crops as weeds, challenging food security. By bridging historically siloed ecological, agronomic, and evolutionary lines of inquiry into feral crops, there is the opportunity to improve food security and understand this relatively understudied anthropogenic phenomenon. Summary The phenomenon of feral crops, that is, free‐living populations that have established outside cultivation, is understudied. Some researchers focus on the negative consequences of domestication, whereas others assert that feral populations may serve as useful pools of genetic diversity for future crop improvement. Although research on feral crops and the process of feralization has advanced rapidly in the last two decades, generalizable insights have been limited by a lack of comparative research across crop species and other factors. To improve international coordination of research on this topic, we summarize the current state of feralization research and chart a course for future study by consolidating outstanding questions in the field. These questions, which emerged from the colloquium “Darwins' reversals: What we now know about Feralization and Crop Wild Relatives” at the BOTANY 2021 conference, fall into seven categories that span both basic and applied research: (1) definitions and drivers of ferality, (2) genetic architecture and pathway, (3) evolutionary history and biogeography, (4) agronomy and breeding, (5) fundamental and applied ecology, (6) collecting and conservation, and (7) taxonomy and best practices. These questions serve as a basis for ferality researchers to coordinate research in these areas, potentially resulting in major contributions to food security in the face of climate change.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.006 | 0.021 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".