Response of rape cultivars with different boron efficiency to boron-magnesium nutrition at seedling stage
Bibliographic record
Abstract
Solution culture to studied the responses of two rape ( Brassica napus ) cultivars with different boron efficiency to boron-magnesium nutrition at seedling period. The results showed that increasing Mg concentration in the solution had no influence on the growth of two cultivars under lower B level. However, B content and B accumulation in plants decreased, and Mg content, Mg accumulation and chlorophyll content increased. Similarly, increasing B concentration under lower Mg level, Mg content, Mg accumulation and chlorophyll content decreased, the decreasing extent in the B-inefficient cultivar was larger than in the B-efficient cultivar, but chlorophyll content in B-efficient cultivar was larger than in the B-inefficient cultivar. When increasing B concentration in the solution under higher Mg level, there was a significant synergism between B and Mg. The effect of the synergism in the B-inefficient cultivar was less than in B-efficient cultivar. To a large extent, chlorophyll content in plant was tighter relative to Mg content, and less relative to B content. Boron-magnesium nutrition had no significant influence on Zn content. Under the low Mg condition, increasing B concentration could enhance Mn content significantly. At two Mg levels, increasing B concentration improved Fe nutrition of the two cultivars, however, Fe content in B-inefficiency cultivar raised significantly.
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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 | 0.001 | 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".