Investigation of Physical Quality Characteristics of Dry Land and Wet Land Wheat Varieties
Bibliographic record
Abstract
The aim of this research study was to determine the physical characteristics of some commercial wheat varieties of dry land and wet land grown in Sindh province. Four irrigated land (Inqulab, TD-1, Kherman, and Sarsabz) wheat varieties and four drought tolerant (TK-3, Marvi, PK-85, Sassi) wheat varieties were collected from their respective areas and subjected to physical analysis.The physical characteristics of dry land and wet land wheat varieties differed significantly. It was observed that dry land wheat varieties higher in length (7.29mm) as compared to wet land wheat varieties (7.05mm). Whereas, wet land wheat varieties higher in breadth (4.97mm), thickness(3.86mm), volume (59.7mm3), geometric mean (10.66mm) and sphericity (1.72%) than those of dry land wheat varieties with breadth (4.15mm), thickness (3.25mm), volume (45.3mm3), geometric mean (9.34mm) and sphericity (1.35%). It is also observed that TKW (47g) of wet land wheat varieties were higher than those of dry land wheat varieties TKW (40.2g). Moreover, falling number (419sec) were recorded higher in wet land wheat varieties than those of dry land wheat varieties falling number (387sec). While, dry land wheat varieties increased in its hardness (55.3%) than those of wet land wheat varieties hardness (51.3%). This study reveals that availability of water and environmental factors are directly related with the nutritional characteristics of wheat varieties. This study revealed that due to more moisture content in wet land wheat varieties TKW, breadth, thickness, volume, geometric mean, falling number and sphericity were recorded as higher than dry land wheat varieties. However, Length and hardness were observed higher in dry land wheat varieties which resulted in better yield of flour as compared with wet land wheat varieties.
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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".