Northern Wild Rice (<i>Zizania palustris</i> L.) breeding, genetics, and conservation
Bibliographic record
Abstract
Abstract Cultivated Northern Wild Rice (NWR; Zizania palustris L.) is a high‐value, small commodity crop grown in irrigated paddies, primarily in Minnesota and California. Domestication of the species began ∼60 years ago as demand for the nutritional grain outpaced hand‐harvesting efforts from lakes and rivers in the Great Lakes region of the United States and Canada. Cultivated NWR cultivars are open‐pollinated and highly heterogeneous and have primarily been bred for seed retention, yield, and seed size. As a lowland crop, NWR's life cycle, particularly its unique seed physiology, poses challenges to breeding efforts, limiting selection cycles per year, and requiring annual grow‐outs of all germplasm. Recent efforts have increased the genomic resources available to NWR researchers, including a reference genome assembly and methodology optimization for genotyping‐by‐sequencing technologies. The species’ close phylogenetic relationship with white rice (Oryza sativa) also provides a unique opportunity to utilize comparative genomic approaches to identify genes conferring agronomic traits of interest in NWR, particularly domestication traits such as seed retention. Z. palustris is an enigmatic species with regional ecological, cultural, and agricultural significance in the Great Lakes. As NWR is grown in both the centers of origin and diversity, it is important for NWR plant breeders to be good stewards of our domesticated plants and include a diversity of stakeholders in our decision‐making processes. In this work, we have aimed to unpack some of the disputes regarding the breeding of cultivated NWR and the science behind our work. Additionally, we have discussed conservation efforts for natural stands of NWR which will help preserve the many ecosystem, nutritional, spiritual, and economic services provided by this important species.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".