Effects of Sowing Date on Protein and its Components Contents in Barley Grain
Bibliographic record
Abstract
To clarify the effect of different sowing dates on protein and its components contents in barley grain,10 different barley cultivars were planted under three different times in Shihezi area.The results showed that:the differences of grain protein contents was obvious under the different sowing dates.The effects of sowing date treatment,cultivars quality and the interaction between sowing date and cultivars on grain salted protein content were all insignificant.The main treatment of sowing date had remarkable effect on grain hordein content,but the effects of different cultivars and its interaction with main treatment appeared to be more obvious;Furthermore,sowing date treatment imposed remarkable effect on barley grain glutelin content.Stepwise regression analyses showed that:during the period of barley earring stage to mature stage,among many meteorological factors,mean maximum temperature per day,comparative difference of temperature each day,average relative humility each day were key meteorological factors to barley grain protein content.In terms of salted protein content,the key meteorological factors were comparative difference of temperature each day and mean sunshine time each day.But as for barley grain hordein content,the factors of mean minimum temperature each day,comparative difference of temperature each day and mean relative humility each day were the key factors.When it comes to barley grain glutelin content,the key factors belonged to comparative difference of temperature each day and mean relative humility each day.The results mentioned above indicated that external environment conditions might regulate barley grain protein and its components contents to a certain extent,so via selecting sowing date in practical production could controlling and regulating barley grain protein traits effectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".