Three-component barley mixtures: Ratio effects in replacement series
Bibliographic record
Abstract
Within a species, cultivar mixtures may offer yield and quality advantages if the cultivars have complementary abiotic and biotic stress tolerances. This study was conducted at Botha, Lacombe and Olds, Alberta, from 1992 to 1994 to determine the effect of relative seeding ratios on yield and other traits of 16 three-component barley (Hordeum vulgareL.) mixtures of Virden:Abee:Tukwa all grown at a standard seeding rate of 250 seeds m–2. Grain yields of these mixtures fell between the yields of the monocrops, with yields of the 20:40:40 and 50:30:20 mixtures being higher than expected based on the weighted mean yields of the monocrops. When stability of yield was measured using ranking or regression analyses, several mixtures had desirable combinations of high yields and good stability with the 20:40:40 and the 40:20:40 mixtures being identified using either method. Test weights, kernel weights, percent thins, protein contents, and disease levels of the mixtures were intermediate to the monocrops; while lodging levels were as low as the best monocrop. As the proportion of any one cultivar in the mixture increased, the traits it brought to the mixture also increased. These mixtures had no yield advantage over growing a high yielding monocrop. Key words: Barley, Hordeum vulgare L., mixtures, cultivar, yield, tolerance, stress
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".