Screening Oat Genotypes for Tolerance to Salinity and Alkalinity
Bibliographic record
Abstract
This study aimed to develop an effective method for determining salt and alkali tolerances in oats, an important food crop. 1) In experiment 1, 68.5mmol.L-1 salt and 22.5mmol.L-1 alkali were identified as appropriate concentrations for determining the tolerance of oats to salinity and alkalinity during germination. 2) To validate the screening method obtained in experiment 1 for use in the germination stage, 248 oat genotypes were evaluated, of which 21 were identified to be tolerant to salinity and alkalinity. 3) In experiment 3, one salt treatment (40L of Na2SO4:NaCl (1:1), 150mmol.L-1) was found to be optimal for determining the tolerance of oats to salinity during the growth and development stage. For alkalinity tolerance, the optimal treatment was 40L of Na2CO3:NaHCO3 (1:1), 75mmol.L-1. 4) Because there was no significant relationship between tolerances at the germination and growth stages, it is essential to use screening methods that combine the two stages. 5) In experiment 4, 25 oat genotypes that were highly tolerant to salinity and alkalinity at both the germination and growth stages were identified from 262 oat genotypes. 6) GGE biplot software was found to be an effective tool for interpreting the results. The plastic cone-tainer planting method was found to improve screening efficiency. 7) There were differences in the effects of salinity and alkalinity on oats. Alkali stress mainly reduces the chlorophyll content, while salinity mainly disrupts water absorption. 8) Chlorophyll content could be used as a physiological criterion for identifying both salt and alkali tolerances.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".