Comparison of Growth Characteristics and Yield Components between High-Yielding and Low-Yielding Varieties of Winter Triticale in Hokkaido, Japan
Bibliographic record
Abstract
Field experiments were carried out over a period of four years in Hokkaido, the northernmost island of Japan,to determine the differences between growth characteristics of high-yielding and low-yielding winter triticale varieties in a snowy region and to determine the criteria for selecting varieties that can produce high yields and the important points in cultivation in order to obtain high yields.Grain yields, characteristics related to grain yield,winter survival indices,and lodging indices of triticale varieties from Poland, Russia, Ukraine, Canada, France, England and Korea were compared with those of wheat and rye varieties.The grain yields of most of the triticale varieties from Poland were higher than that of Hokushin, one of the main winter wheat cultivars grown in Hokkaido, and those of rye varieties.However, the grain yields of triticale varieties bred in other countries were lower than that of Hokushin.The high grain yields of Polish triticale varieties were thought to be due to greater one-ear weights and plant weights than those of the wheat varieties and due to higher harvest indices than those of the rye varieties.The low grain yields of other triticale varieties were thought to be due to a high incidence of snow mold disease,small number of ears,and low harvest index.Lodging also contributed to the reduction in grain yields of long-culmed triticale varieties.Based on these results, it is thought that high yields of triticale grown in a snowy region of Hokkaido can be obtained by selecting triticale varieties that have a high degree of snow tolerance, a large number of ears and a high harvest index and by taking measures to prevent a reduction in the number of ears due to winter injury. Key words:winter
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".