COTTON GERMPLASM CHARACTERIZATION FOR DROUGHT TOLERANCE BASED ON MORPHO-PHYSIOLOGICAL AND FIBER QUALITY PARAMETERS
Bibliographic record
Abstract
Drought tolerance is a quantitative trait that is exceedingly challenging to breed, especially for allotetraploids like cotton. The scenario of limited water resources necessitates developing droughttolerant cultivars that conserve significant irrigation water throughout the summer. Therefore, the presented study used a design to statistically analyze the morphological, physiological, and fiber quality parameters linked with drought tolerance, which is a comprehensive method for choosing better genotypes from the available cotton germplasm. Measuring these parameters ensued for plants grown under field conditions. The germplasm comprised 150 cotton genotypes studied at two water regimes, i.e., regular and water-stressed conditions for two consecutive seasons of 2015–2016 and 2016–2017. Data recording ran for different morpho-physiological and fiber quality parameters. Significant differences occurred for all the treatments, genotypes, and Genotype × Environment interaction for all the morphological, physiological, and fiber quality parameters under study. Additive Main effects and Multiplicative Interaction (AMMI) analysis and AMMI biplot analysis helped analyze the results, which revealed that the cotton genotypes FH-900, FH-901, FH-312, AS-1, AS-2, AS-3, RH510, RH-627, AR-2, AR-9, BH-118, BH-175, SLH-74, CIM-1100, CIM-598, and MM-58 were drought tolerant and ranked highest concerning stress condition. Moreover, correlation studies distinguished the relationship between relevant traits concerning drought tolerance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".