Effect of N<sub>20</sub> P<sub>20</sub> K<sub>20</sub> Nano-Fertilizer on Quantitative and Qualitative Yield of Two Potato Cultivars
Bibliographic record
Abstract
Abstract The study was conducted in the vegetable field of the Department of Horticulture and Landscape Design, College of Agriculture and Forestry, University of Mosul, during the 2021 agricultural season, in order to study the effect of spraying Nano-fertilizer N 02 P 02 K 02 at concentrations of 1, 2, 3 and 4 g·L −1 compared to control treatment, in some components and quality of two potatoes varieties (Florice and Montreal). The experiment was conducted in a split - plot system within RCBD Design with three replications, cultivars factor arranged in the main plots and the concentrations of Nano-fertilizer in the sub plots. After recording the data and analyzing it statistically, the results were as follows: Florice variety was significantly superior in the total number of tubers, while Montreal variety was significantly superior in the percentage of dry matter, starch content of tubers and the specific weight of tubers. The use of the Nano-fertilizer at concentration of 2 g·L −1 led to a significant increase of the number of total, and marketable tubers by 4.65 tubers by plant and 11.10 tubers by plant, respectively compared to unfertilized control. The concentration of 3 g·L −1 led to a significant increase of the mean tuber weight, plant yield, marketable tubers yield, and tuber firmness to 108.54 g, 1189.38 g and 63.434 t·ha84. 10dna kg·cm −2 respectively, compared to unfertilized control. The interaction treatment between Florice cultivar and Nano-fertilizer administered in concentration of 2 g·L −1 led to a significant increase in the number of total and marketable potato tubers. The treatment of Montreal cultivar with a concentration of 3 g·L −1 Nano-fertilizer led to a significant increase of the mean tuber weight, plant yield, total yield of tubers and TSS in tubers, percentage of dry matter and starch in the tubers and the specific weight of the tubersd
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".