Effects of Cut off the Irrigation in Different Growth Stages on Yield and Yield Components of Rapeseed Cultivars
Bibliographic record
Abstract
Iran is a country of water scarcity due to general low precipitation, high evaporation and the temporal and spatial distribution of rainfall. In order to determine the effects of disruption of irrigation in different growth stages of autumn’s rapeseed cultivars, an experiment was conducted in 2009-2010 at Isfahan agriculture research station. A split plot layout within a randomized complete block design 3 replications was used. Main plots were seven levels of cut off irrigation namely, D1= current irrigation or irrigation after 80 millimeter vaporize from class A basin to physiological maturity, D2= cut off irrigation from stem elongation phase and then on, D3= cut off the irrigation from flowering and then on, D4= cut off the irrigation from pod formation phase and then on, D5= cut off the irrigation in stem and flowering phase, D6= cut off the irrigation in stem and pod phase, D7= cut off the irrigation in flower and pod formation, and sub plots were two rapeseed cultivars, namely, Zarfam and Okapi. With increasing the number of irrigation, rapeseed yield will increase, but if the water lacks occurred, it is better not to cut off irrigation in flower and stem phase, in order to get acceptable seed and oil yield. Zarfam had the highest oil and seed yield in withheld irrigation conditions and also have the best adaptation in water deficit conditions.
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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 | 0.001 |
| 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".