Effect of GA3 dipping and spraying with KT-30 on characteristics of vegetative growth and yield to three types of potato ( Solanum tuberosum L.)
Bibliographic record
Abstract
The experiment was conducted in the fields of the College of Agriculture / Tikrit University for the spring season 2019 Within an area with longitude (43.35 degrees east of Greenwich mean) and latitude (34.27 degrees north of the equator), to study the effect of dipping with GA3 and spraying with KT-30 with three levels for each other (0, 5, 10) mg. L-1 in some characteristics of vegetative growth, yield and productivity of three potato cultivars (Barcelona, Laperla, Montreal). The experiment was carried out using a split-split plot design within a randomized complete block design (R.C.B.D.) and with three replicates. The cultivars put in the main plots. And dipping with gibberellin in Sub- plot and spraying with KT-30 in the Sub-Sub plot, the results showed that dipping with gibberlin caused significant differences in plant length and leaf area, and the values were as follows (35.19 cm and 10263.00 cm2), respectively, while spraying with KT-30 was superior in the characteristic of the leaf area, which amounted to (10140.10 cm2), and the cultivar Lapirla gave a significant increase. In the number of aerial stems and leaf area, the values reached (4.60 stem. Plant-1 and 11366.90 cm2) respectively. As for the characteristics of the outcome, dipping with gibberlin and spraying with KT-30 had no significant differences. As for the cultivars, the cultivar Laperla surpassed the other two cultivars in yield. One plant, the number of total tubers and the total yield and the values were (1.18 kg. Plant-1 and 14.16 stems. Plant-1 and 46.12 tons. E-1) respectively., as for the bifurcation double and triple between treatments, the increase was significant in plant height, number of stems and leaf area, and for the yield traits also gave significant difference in the yield characteristics, yield of plant, number of tubers and total yield.
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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) | 0.000 | 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 | 0.001 |
| 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".