The Effect of Shading, Organic Fertilizer( vermicompost ), and Chelated Iron on the Vegetative Growth Characteristics of Gardenia jasmoida
Bibliographic record
Abstract
The experiment was conducted in a private nursery (Mohammed Nursery) located on Erbil Road in Kirkuk, situated at 44.38°E longitude and 35.58°N latitude. The experiment lasted five months from 01/04/2022 to 01/10/2022 to study the effects of shading with two levels: 0% (under direct sunlight) and 75% (under saran cover), as well as the effect of organic fertilizer (vermicompost) at two levels: 0% and 25%, where the fertilizer was added and mixed with loamy soil. The experiment was conducted using a completely randomized block design. The plants were randomly arranged on the experimental units with three replicates. Duncan's multiple range test was used to compare the means at a probability level of 0.05%.The results indicated that the 75% shading had a significant effect on some characteristics, such as plant height, dry leaf weight, and leaf area, which reached (82.27 cm), (12.14 g), and (3086.10 cm²) respectively. On the other hand, plants grown under direct sunlight showed a significant increase only in stem diameter (10.69 mm).Regarding the effect of organic fertilization, a significant increase was observed with the use of 25% vermicompost on the following characteristics: plant height (73.55 cm), dry weight (12.63 g), and chlorophyll content in leaves (29.24 CCI).It was also found that the use of chelated iron had a significant effect on most characteristics, where the concentration of 0.2 g.L⁻¹ resulted in the highest significant increase in plant height (75.00 cm), main stem diameter (10.37 mm), dry weight (15.35 g), and chlorophyll content in leaves (29.10 CCI).
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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 | 0.000 |
| 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".