Studies on the Nutritional Characteristics of some Commercial Wheat Varieties of Dry Land and Wet Land Grown in Sindh Province
Bibliographic record
Abstract
The present research was carried out to investigate the nutritional characteristics of some commercial wheat varieties of dry land and wet land grown in Sindh province during 2011-12 at Institute of Food Sciences and Technology, Faculty of Crop Production, Sindh Agriculture University Tandojam. Four irrigated land (Inqulab, TD-1, Sarsabz and kherman) wheat varieties and four dry land (TK-3, Marvi, PK-85, Sassi) wheat varieties were collected from their respective areas and subjected to chemical analysis.The bio-chemical characteristics of dry land and wet land wheat varieties differed significantly. Chemical analysis indicate that moisture (13.06%), protein (14.83%), dry gluten (9.03%), wet gluten (35.66%), gluten index (73.8%), starch (75.83%) and zeleny (68.66%) contents were recorded higher in wet land wheat varieties than those of dry land wheat varieties with moisture (12.66%), protein (11.9%), dry gluten (8.2%), wet gluten (32.93%), gluten index (64.53%), starch (68.66%) and zeleny (58.33%). This study reveals that availability of water and environmental factors are directly related with the nutritional characteristics of wheat varieties. This study clarify that wet land wheat varieties are better in the context of nutritional qualities.
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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 | 0.001 |
| 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".