COTTON GENOTYPES APPRAISAL FOR MORPHO-PHYSIOLOGICAL AND YIELD CONTRIBUTING TRAITS UNDER OPTIMAL AND DEFICIT IRRIGATED CONDITIONS
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Bibliographic record
Abstract
In agricultural ecosystems, drought has a detrimental effect on crop production, affecting the growth rate and development of the economically important traits of the crop plants. The presented study aimed to assess the genetic potential and aspects of 20 upland cotton cultivars (Gossypium hirsutum L.) for morpho-physiological and yield contributing traits under optimal and deficit irrigated conditions during 2018–2019, at Tashkent, Uzbekistan. With water deficit conditions, the proline content in plant leaves of various cotton genotypes increased (76.36%) compared with the optimal water regime. The chlorophyll a and b, total chlorophyll, and carotenoids can increase and decrease to varying degrees, depending upon the water content in the leaves of cotton genotypes. Results also revealed that upland cotton’s leaf relative water content, excised-leaf water loss, total chlorophyll, chlorophyll a and b, carotenoid and proline contents, plant height, sympodial branching, leaf area, bolls per plant, opened bolls plant, and seed cotton yield depended on water supply conditions and the genotypic composition of the genotypes. Based on the analysis of stress tolerance indices for morpho-yield and some physiological traits of cotton genotypes under different irrigation regimes, genotypes Namangan-77, Hapicala-19, 0-30, Zangi-Ota, Saenr Pena-85, S-2025, KK-602, SAD-35-11, and C-417 revealed tolerant to water deficit conditions. However, the cotton cultivars KK-1796, KK-1795, 1000, L-N1, S-9006, KK-1086, Catamarca 811, S-9008, L-N1, 141, C-4769, and L-45 were not good performers and susceptible to water stress conditions. Results concluded that soil drought conditions during the flowering stage disrupted physiological processes, including leaf relative water content and excised-leaf water loss.
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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 | 0.001 | 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 it