Screening of Cotton Genotypes for Protein Content, Oil and Fatty Acid Composition
Bibliographic record
Abstract
The increase in the population at the global level necessitates to explore promising approaches to increase food supply, including protein and oil, to meet the needs of the people. Cotton is one of the most important oil producing crops and cottonseed meal provides important protein nutrients as animal feed. However, information on the genetic basis of cottonseed oil and protein contents is lacking. In this study; protein contents, oil and fatty acid composition of 124 cotton genotypes were observed for developing new cultivars. Accelerated Solvent Extraction method used for determining fat ratio; Gas Chromatography employed for fatty acid analysis while protein contents were analyzed by Kjeldahl method. Average crude oil 31.0%, total fat contents varied from 23.11 to 37.70% while mean protein content 38.0% were observed among genotypes. The dominating fatty acids included linoleic acid, palmitic acid and oleic acid (46.91, 25.73 and 20.21%) respectively, while linolenic acid (0.13%), r-linolenic (0.33%), palmitoleic acid (0.64%), myristic acid (0.88%), nervonic acid (1%) and stearic acid (2.38%) had variations in fatty acid contents. Frequency distribution of the parameters showed a normal distribution and differences among genotypes for the traits studied were statistically highly significant. Prinicipal component analysis showed a complex opposite relationship with a total protein and oil contents. Genotypes; Fantom for protein, Cirpan 60 for total crude oil, Stoneville 468 and YB195 for higher amount of fatty acids especially oleic acid; can be used for improvement of cottonseed quality in breeding programs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".