Nutritional quality of maize in response to drought stress during grain-filling stages in mediterranean climate condition
Bibliographic record
Abstract
Maize is considered one of the most essential dietary components in human food and animal feeding.The objectives of the present study were to quantify the effects of drought stress on qualitative traits of maize at grain-filling stages.Hybrids maize seeds were grown by applying full and water stress conditions during the grain filling stage.Various nutritional properties (crude oil, starch, grain protein content) were determined in 2014 and 2015 at the second crop growing season in Adana, Turkey.Based on the results of this study, genotype and environment were found to influence all quality traits significantly.Further, result of study suggest that water stress caused a significant reduction in major quality traits.Grain weight and grain quality yield as well crude oil, protein and ash yield were significantly decreased due to water deficit condition in the both growing seasons.Significant differences were observed among hybrids in respect of all measurements due to irrigation regimes.The genotypes, Sancia and Calgary were tolerant by producing higher grain weight.Accordingly, grain qualities of 71May69, Aaccel and Calgary maize hybrids were less affected under drought stress.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".