The Effect of Enriched Organic Fertilizer and Methanol Spray on the Greenhouse-Tomato Yield
Bibliographic record
Abstract
An experiment was conducted in the form of split-plot and random complete blocks with 3 repetitions in a greenhouse in Jiroft city of Kerman province, southeastern Iran, during 2011-2012 crop year, with the main aim to study how enriched organic fertilizer (chicken manure) and spraying methanol affects the quantity and quality of the yielding in Falcato tomato. Methanol and the enriched chicken manure as the two main and subordinate factors were studied in 4 levels (0, 20, 30, and 40 percentage of the volume) and (0, 500, 1500, and 2500 kg/ha). The results showed that the interaction of the two factors on the number of fruits per plant, the mean weight of fruit (p ? 0.05), and the stem diameter (p ? 0.01) was significant. Methanol 30% caused a 28% increase in yield, compared to the control plant. Adding 2500 kg/ha chicken manure resulted in 31% increase in the yield in contrast to the control plant. Mean weight of a fruit in 40% methanol and 2500 kg/ha chicken manure treatment was 145 g. Finally, having evaluated all aspects and factors in this study, methanol 30% solution and 2500 kg/ha chicken manure were recommended for Falcato tomato in greenhouses of Jiroft city.
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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) | 0.001 | 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".