Developing a Screening Tool for Osmotic Stress Tolerance Classification of Rice Cultivars Based on In Vitro Seed Germination
Why this work is in the frame
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Bibliographic record
Abstract
Dry direct seeding is the common practice for growing rice ( Oryza sativa L.) in the US Mid‐South. Dry soil conditions during sowing may cause delayed and nonuniform seed germination that can be further aggravated by the low temperature conditions. Understanding the response of rice cultivars to drought stress during seed germination would be useful in optimizing direct seeding practices. An in vitro experiment was conducted to study the impact of osmotic stress using polyethylene glycol on seed germination traits of 15 rice cultivars commonly grown in the US Mid‐South production system. Time series data for seed germination were generated at a wide range of osmotic potentials (0 to −1.0 MPa with −0.2‐MPa increments). Seed germination rate, maximum seed germination, maximum osmotic potential when seed germination was zero, and maximum osmotic potential when seed germination rate was zero were derived based on regression techniques between these parameters and osmotic potential. The rate of maximum seed germination and the seed germination rate decreased significantly with decreasing osmotic potential. A cumulative drought response index was developed by summing individual response indices of parameters. It was used to classify cultivars into three drought‐tolerant groups: high, medium, and low. Among the 15 cultivars tested, Cheniere was identified as least tolerant and RU1204122 as the most tolerant to drought. The identified tolerance among the rice cultivars would help the rice producers in selecting the cultivar that can best germinate in a specific environment and would help rice breeders in developing drought tolerant cultivars for variable climatic conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it