Effect of Different Zinc Concentration and Density on Biological Characteristics and Yield of Tartary Buckwheat
Bibliographic record
Abstract
[Objective] To explore the best combination of tartary buckwheat which plant density and zinc soaking concentration,and provide a theoretical reference.to optimize cultivation techniques and improve yield.[Method] XiQiao II as the studied material,to study the effect of different Zinc concentration and density of buckwheat on the biological characteristics and yield through randomized complete block design.Experimental data were analysed by the software Excle statistics and analysis of variance(SSR method).[Results](1)Plant height and leaf number increased with the density increasing.The primary branch number decreased with the increase of the density.Leaf area increased with the density at 1.2-1.8 million/hm2 range,and then reduced.When the zinc concentration in the range of 0 to 0.4%,tartary buckwheat plant height,leaf number,leaf area,primary branch number and yield increased with the concentration,and showed downward trend with zinc concentrations continue to increase.(2) Different density and concentration had significant effect on production of Xiqiao II.The change of yield and density showed low-high-low trend,and production decreased when the density was greater than 1.8 million/hm2.The change of yield and concentration showed low-high-low trend,and yield declined when the concentration was 0.4%.[Conclusion] The combination treatment both affected the biological characteristics and yield of tartary buckwheat,according to tartary buckwheat growing and production perspective,the best combination of west buckwheat II was density 1.8 million/hm2,zinc soaking concentration 0.4%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".