Bibliographic record
Abstract
The aim of this study was to characterize and improve the flour of three Sudanese wheat cultivars for bread making.Three local wheat cultivars; Debaira, Wadi Elneel, and Elneelain, and Canadian wheat (as control) were treated with three improvers: Alpgida (A), Samabeel (S), and Zena (Z).The results showed significant (P ≤ 0.05) difference in the quality tests among the flours and breads made from the three local cultivars and Canadian wheat flours.The local cultivars found to contain low alphaamylase activity (676 to 486sec), sedimentation value(19.6to32.3cm 3 ), Pelshenke test value (28.8 to 49.2min), water absorption(63.9 to 66.0%), resistance (140 to 208cm), extensibility(120 to 183mm), and high degree of softening(85 to 106FU).However the Canadian wheat cultivar was found to contain low alpha-amylase activity ( 603sec),water absorption (61.3%), degree of softening (38FU), relatively high sedimentation value (37.4cm 3 ), resistance (252cm), extensibility (235mm), and high Pelshenke test value(92.1min).Addition of improvers to the three local cultivars and Canadian wheat flours significantly (P ≤ 0.05) affected the quality tests with the exception of sedimentation value.Sensory evaluation of flat bread showed thatflat bread made from Debaira cultivar with S improver gained the highest score of general acceptability (8.0).Generally, Debaira cultivar showed better bread making quality as compared with the other two local cultivars.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.902 | 0.888 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".