Low temperature and excess moisture affect seed germination of soybean (<i>Glycine</i> <i>max</i> L.) under controlled environments
Bibliographic record
Abstract
Cold and excess moisture pose a serious threat to soybean production especially during seed germination in short-season environments. In this study, the effects of low temperature and excess moisture stress on seed germination were investigated in 187 soybean accessions originating from 18 countries. The experiment used a combination of three temperature conditions (i.e., 20 °C/14 °C, 14 °C/10 °C, and 10 °C/10 °C day/night) and two moisture levels (i.e., normal and excess). The seed germinability traits measured included germination rate (GR), germination index, germination time, germination uniformity, and coefficient of velocity of germination. Overall, GR was lowest in the 20 °C/14 °C + excess moisture and germination time was longest in the 10 °C/10 °C + excess moisture. When compared with 20 °C/14 °C + normal moisture treatment, GR at 10 °C/10 °C + excess moisture decreased by 38%; germination time increased by 20 days; seed viability decreased by 83%; germination uniformity decreased by 70%; germination speed decreased by 73%. Differences in GR, germination index, and germination velocity under different treatments were affected by temperature, moisture, and their interaction. Variation in germination time uniformity was determined by temperature, with no significant effects of moisture conditions and the interaction of temperature and moisture. It was shown that the temperature–excess moisture interaction led to a sharp decrease in seed germination. Two genotypes including PI 603147 and PI 507702 were identified with a GR over 90% at 10 °C/10 °C + excess moisture. This study generated new knowledge and data to further the understanding of genetic resistance to cold and excess moisture stress in soybean.
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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 | 0.000 |
| 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".