Influences of Different Nitrogen Levels on Competition between Spring Wheat (Triticum aestivum L.) and Wild Mustard (Sinapis arvensis L.)
Bibliographic record
Abstract
A field experiment was carried out to investigate the effects of different nitrogen levels on interspecific competition between wild mustard and spring wheat. The experiment was laid out in randomized complete block design under a split plot arrangement, with three replications. The experiment comprised of three nitrogen levels (90, 150 and 210 kg ha-1) assigned to main plots and four wild mustard densities such as 0, 5, 10 and 15 plants m-2 kept in sub-plots. The results revealed that yield, yield components and nitrogen use efficiency of wheat were decreased with increasing wild mustard density. The density of 5, 10 and 15 wild mustard plants m-2 reduced the grain yield of wheat by 21.4, 32.2 and 40.2% respectively as compared to control. Increasing nitrogen level increased the grain yield of wheat in weed free plots, while in the presence of wild mustard, increasing nitrogen level led to increase the competitive ability of wild mustard and increased the yield losses of wheat. The density of 15 wild mustard plants m-2 had decreased the grain yield of wheat by 31.6, 34.4 and 53.3 % under 90, 150 and 210 kg N ha-1 respectively.
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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) | 0.001 | 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) | 0.001 | 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".