A comparison of soybean maturity groups for phenology, seed yield, and seed quality components between eastern Ontario and southern Manitoba
Bibliographic record
Abstract
The expansion of soybean [ Glycine max (L.) Merr.] production onto the Canadian Prairies has resulted in new environmental constraints that affect soybean phenology, seed yield, and seed quality. This study examined these factors for 10 soybean cultivars differing in maturity group (MG) rating from 000.9 to 1.3 in southern Manitoba (MB) and eastern Ontario (ON). Detailed climate and phenological data collected at both locations were used to explore the environmental factors and differences in measurements among MG and between locations. In MB, more time was spent in vegetative growth and less time developing flowers and seeds than in ON. The longer vegetative growth stage in MB resulted in more leaves produced on the main stem at flowering than in ON. The leaf appearance rate was consistent between locations and the rate of phenological development in the vegetative stage was greater in ON because of its warmer mean temperature and shorter photoperiod. In MB, seed yield was positively correlated with precipitation in all growth stages and had a strong correlation with precipitation during reproductive development. In ON, increasingly warmer temperatures during reproductive development had the greatest influence on seed yield, particularly in the seed development stage. This study is a baseline for soybean phenology, seed yield, and seed quality components for early MG and will aid in the optimization of soybean breeding and production in the Canadian Prairies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".