Milestones in Kernza® Perennial Grain Development
Bibliographic record
Abstract
This timeline was created to document the development of Kernza® perennial grain, harvested from improved varieties of intermediate wheatgrass. It includes agronomic, genetic, environmental, food science, and social research along with farmer adoption, policy, supply chain coordination, and commercialization. This work was achieved through the collaboration of dozens of researchers, farmers, business partners, policy advocates, and more. The timeline includes extensive dates from early work at the Rodale Institute during the 80’s and 90’s, such as cycles of selection and crosses made. Key milestones are noted from the early 2000’s to 2010’s, when research shifted to The Land Institute. Increasingly detailed information on events, publications, network development, commercialization, research, and other topics is available from the early 2010’s to 2022, as Kernza breeding programs were initiated at several universities, commercial production began, and formalized market and supply chain development efforts accelerated. This timeline focuses on US work, but Kernza research and production have expanded to multiple countries, including Canada, France, Sweden, Ukraine, and Uruguay. Of course, this timeline does not capture everything of importance that occurred during this period. The timeline was compiled by KernzaCAP collaborators in 2022 through a combination of crowdsourcing, interviews, and conversations with people in the Kernza network (including researchers, policy advocates, farmers, commercialization staff, chefs, business owners and end users) as well as review of peer-reviewed publications, emails, meeting agendas, popular press articles, Minnesota State Legislature records, and web pages of universities, nonprofit organizations, and government agencies. Though the information is accurate to the best of our ability, the length of the timeline and its crowd-sourced nature introduce the possibility of errors. Where possible, links are included to the source, such as a peer-reviewed article, newspaper article, or other publication. For a more condensed version of this timeline that celebrates the past 40 years of work in the United States, see: https://hdl.handle.net/11299/256021 KernzaCAP is supported by AFRI Sustainable Agricultural Systems Coordinated Agricultural Project (SAS-CAP) grant no. 2020-68012-31934 from the USDA National Institute of Food and Agriculture.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".