Effect of Soaking Tubers in Potassium Humate and Foliar Application of Nano-Calcium Fertilizer on some Growth Traits of Two Potato Cultivars
Bibliographic record
Abstract
Abstract In the spring of 2022, researchers from the University of Mosul’s Faculty of Agriculture and Forestry conducted an experiment on a vegetable field. In this experiment, we looked at three variables: first, the effects of two different potato cultivars (Montreal and EL-Beida). For the second part, we soaked potatoes in a solution of potassium humate with a concentration of (0, 0.5, 1 g L -1 ). Therefore, the experiment had 18 treatments (2 3 3), with the third factor being Nano-calcium fertilizer with three concentrations of (0, 1.5, and 2.5 g L -1 ) applied to plants at three stages of plant growth: the first 20 days after full germination, the second and third stages, with a 20-day interval between addition and another. Cultivars were positioned in the primary plots, with the interaction between two additional variables located in sub-plots, as part of a factorial experiment inside a split-plot utilizing the Randomized Complete Block Design with three repetitions. Duncan’s multiple range test for comparing means was used to analyze the data at a 5% significance level. This leads to the following conclusion: Of the cultivars tested, the Montreal variety performed best when soaked in potassium humate at two different concentrations, increasing both leaf area and dry matter percentage in the vegetative development (0.5 and 1). both total chlorophyll content and leaf area were significantly increased by g.L -1 . The best statistically significant data for plant height, number of aerial stems, and leafy area were obtained after spraying calcium Nano-fertilizer at two doses.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".