Growing quinoa in Washington State
Bibliographic record
Abstract
Quinoa (Chenopodium quinoa Willd.) is gaining popularity as a relatively new crop for Washington State. It has been cultivated for thousands of years around its center of origin in South America, but recently has gained worldwide recognition for its nutritional benefits and adaptability to a variety of environments. Quinoa production in North America was very limited until recently. Quinoa has been successfully cultivated in regions such as the Canadian prairies, the San Luis Valley of Colorado, coastal areas in central California, Willamette Valley of Oregon, and the Olympic Peninsula in Washington. Other areas of Washington State could also provide the right climate and conditions for producing quinoa, such as the maritime climates found along much of the western region, the mountainous regions in central and northern areas of the state, and the Palouse River Basin on the eastern edge. Quinoa may be a suitable crop for a variety of cropping systems that can be found in Washington State. Quinoa is known for producing quality yields even in adverse conditions, including low fertility, throughout the world. For any system, quinoa can be a beneficial rotation crop to help break cereal disease cycles, and even the conventional market value is high compared to similar crop types.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".