Effect of Treatment with Growth Regulators Gibberellic Acid and CPPU on some Vegetative Traits and Yield of Three Potato Cultivars Solanum tuberesom L.
Bibliographic record
Abstract
Abstract The experiment was conducted in the fields of the College of Agriculture / University of Tikrit for the spring season 2019 within an area with longitude (43.35 degrees east of Greenwich) and latitude (34.27 degrees north of the equator), to study the effect of treating potato tubers by soaking with gibberellin before planting and spraying with growth regulator CPPU with three levels for each of which (0, 5, 10) mg L−1 in some vegetative growth characteristics and yield characteristics, quantity and quality of three potato cultivars of class A (Barcelona, Laperla, Montreal). The experiment was implemented using split-split plot design within a design Random complete plots (R.C.B.D) and with three replications, the cultivars were placed in the main plot, dipping with gibberellin in the sub-plot, and spraying with CPPU in the sub-sub plot. The results shows that dipping with gibberellin makes significant differences in the leaves number and percentage of seeding that gave (158.98 plant leaf−1 and 7.21%) respectively. As for spraying with CPPU, it was superior in terms of the leaves number, the number of unmarketable tubers, the percentage of seedig and the percentage of protein, and the values were given (157.39 Plant leaf−1, 2.88 Tuber plant−1, 7.24%, and 6.59%) respectively. As for cultivars, the cultivar Laperla excelled in the following characteristics (the leaves number, the ratio of root weight to the weight of vegetative growth, and the percentage of seeding at harvest). The values were (203.05 plant leaf−1, 31.70%, and 5.59%), respectively. As for the double and triple interactions between the treatments, the increase was significant for all the traits taken for the study.
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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".