Processing Quality of Spring Wheat in Qinghai Province
Bibliographic record
Abstract
One hundred and fifteen spring wheat varieties and lines from China and other countries were sown in (Xining) in 2000, and were used to investigate grain quality and steamed bread-making and noodle-making quality. In general they are characterized with weak gluten and poor steamed bread and noodle quality, but wide range of variations among varieties is observed. A few wheat varieties including Xiaobingmai 33, M99-90, Gaoyou 503, Gelenlea, Canadian 5 and Wildcat perform good steamed bread and noodle quality. Correlation analysis (indicates) that protein content and protein quality have significant effect on steamed bread quality. In experimentⅠ, farinograph stability is significantly (correlated) with steamed bread volume, color , shape and toughness, with correlation coefficient of 0.49,0.58,0.45 and 0.46, respectively. Protein quality is the major factor affecting noodle texture. There are significant positive (correlations) between starch (properties) and noodle quality. For example, peak (viscosity) is significantly (correlated) with palate, toughness, stickiness and smoothness of cooked noodle, and (correlation) coefficients are 0.58、0.61、0.59 and 0.48, respectively. Increasing protein quality and starch (properties) contributes to noodle quality improvement. Ecological condition has significant and negative effect on color, gluten quality and starch viscosity, but it is possible to improve the steamed bread and noodle quality of spring wheat varieties in Qinghai province through genetic improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".