Kernza® Perennial Grain in 40 Milestones
Bibliographic record
Abstract
This timeline highlights key milestones in the development of Kernza perennial grain. It was created to spotlight and celebrate the past 40 years of work in the United States. Launching a new perennial grain crop requires agronomic, genetic, environmental, food science, and social research with a network of community-based expertise in farmer adoption, policy, supply chain development, and commercialization. This effort is only possible through the collaboration of dozens of researchers, farmers, business partners, policy advocates, and more. This timeline provides a sense of the efforts and meaningful moments that mark Kernza’s history through 2022. However, it cannot fully capture the extensive work that went into each achievement, nor many other important milestones that were not included due to space considerations. This timeline focuses on US work, but Kernza research and production have expanded to multiple countries, including Australia, Canada, France, Sweden, Ukraine, the United Kingdom, and Uruguay. Collaborators on KernzaCAP, a multidisciplinary research project funded by the US Department of Agriculture, compiled these milestones in 2022.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.088 | 0.025 |
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".