Durum wheat quality I: some physical and chemical characteristics of Syrian durum wheat genotypes
Bibliographic record
Abstract
Summary Some physical and chemical characteristics of nine Syrian durum wheat genotypes were determined in order to investigate the relationships among individual kernel components and kernel quality. All genotypes were grown on fully irrigated plots in Syria. Test weight, 1000‐kernel weight, kernel size distribution, hardness and semolina extraction rates were determined along with the chemical characteristics of the kernel (ash, moisture, protein, wet gluten, starch content and falling number). All tested genotypes had high test weight (83.1–85.9 kg hl−1) and 1000‐kernel weight (42.5–55.5 g) indicating high milling yield potential. Additionally, all the wheat genotypes demonstrated high falling numbers (433–597 s). Correlation coefficients among the quality properties showed that moisture content did not demonstrate any strong correlations with the studied quality parameters. Protein content exhibited a positive correlation with vitreousness (r = 0.78), and a negative correlation with ash content (r = −0.57). Test weight exhibited a negative correlation with 1000‐kernel weight (r = −0.66), and a positive correlation was observed between test weight and starch content of the kernel (r = 0.78). The results illustrate the commonality between Syrian durum wheat genotypes and US and Canadian durum wheat genotypes reported by researchers previously.
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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".