Soybean (<i>Glycine max</i> L.) seed germination in response to waterlogging and cold climate: a review on the genetics and molecular mechanisms of resistance to the abiotic stress
Bibliographic record
Abstract
Soybean ( Glycine max L.) is the most important legume crop in the world and provides protein and oil for human consumption and animal feed. Cold and waterlogging or flooding are abiotic stress that are commonly encountered during soybean germination in short-season growing conditions in the Northern latitudes. Imbibition of cold water during the germination disrupts the cell membranes and increases leakage of their contents and makes seeds vulnerable to biotic stress. The cold tolerance is associated with the ability of cells to avoid or repair the damage to their membranes and organelles, restoring membrane function and metabolism, and managing the reactive oxygen species generated during the process. Excess moisture impedes aerobic respiration by oxygen deprivation and increases the likelihood of soil-borne diseases further reducing the germination rate. Tolerance to waterlogging is associated with mechanisms that slow down the rate of water uptake and help maintain efficient anaerobic metabolism. The quantitative trait loci mapping, transcriptomics, and proteomic studies have revealed several genes and pathways that likely play a role in seed response to cold and waterlogging stress. This review discusses the effects of cold and waterlogging on soybean seed germination at the physiological level, describes the molecular mechanisms involved, and provides an overview of soybean waterlogging and cold tolerance research. The methodologies commonly used to study the molecular mechanisms controlling tolerance to waterlogging and cold stress are also reviewed and discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".