Using Sulphonated Silicon Nutrient Solution with S8 Elemental Sulfur and Changing Planting Arrangement in Potato Winter Cultivation
Bibliographic record
Abstract
This experiment with aims to increase the tuber yield and quality by using sulphonated silicon nutrient solution with S8 and changing the planting arrangement in potato winter cultivation was investigated in Potato Research Station of Ardabil Province, IRAN during 2022. This experiment was carried out based on the factorial experimental design in three factors and three repetitions. The first factor with two levels including: (1) Spring cultivation and (2) Winter cultivation; the second factor consists of two levels: (1) The planting arrangement of one row on one stack, and (2) The arrangement of planting two rows on one stack in a zigzag pattern and the third factor with three levels includes: (1) Spraying on tuber and soil before planting and foliar spraying in three stages of vegetative growth, tuberization and tuber bulking with a dose of 5 liter nutrient solution in 1000 liters of water per hectare, (2) Foliar spraying in three stages of vegetative growth, tuberization and tuber bulking with a dose of 5 liter nutrient solution of S8 in 1000 liters of water per hectare, and 3. Control (without sulphonated silicon nutrient solution of S8) were. The irrigation method was in the form of drip irrigation. During the growth period, plant height, number of main stems per plant, tuber number and weight per plant and tuber yield were measured. By using nutrient solution and changing the planting arrangement (two rows on one stack) increased tuber yield, tuber number and weight per plant, plant height and water use efficiency in winter and spring cultivation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".