Crop Diversification in Ontario: Adaptation of Chives
Bibliographic record
Abstract
Chives, ( Allium schoenoprasum ) consumption and production are increasing in Ontario. Rust ( Puccinia allii F. Rudolphi) has been a problem with some chive cultivars for some growers, and in Ontario, basic information on production is nonexistent. The objectives were to identify cultivars with high yields, disease resistance and winter survivability. Plantings of six cultivars of chives were established in 2002 and 2003 in two contrasting environments, on organic (Kettleby) and mineral (Simcoe) soils; and one cultivar of garlic chives ( A. tuberosum ) at Kettleby. Leaves were harvested to a length of 30 cm, weighed and assessed for visible signs of rust. In Spring 2003, the number of dead plants was recorded to determine the overwinter survivability of each cultivar. Performance varied among cultivars and between locations. In Simcoe, Staro produced the highest yield in 2002 while generic (unnamed) chives produced the highest yield in the second year. In Kettleby, yield was similar among cultivars in 2002 but in 2003 generic chives produced the highest yield. Overwinter survival also varied between locations and second season yields were much higher in Kettleby. Less snow cover and subsequent winter injury is a possible explanation for the lower yields and poorer winter survival in Simcoe. No symptoms of rust were found in either location. Chives are a viable crop in Ontario, and appear to have different adaptability to regional soils and climates.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 0.000 |
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".