Yield Potential of Shallots Bulbil Planting Materials with Liquid Organic Fertilizer Treatment out of Season
Bibliographic record
Abstract
The main problem with shallots in Indonesia is planting material. The use of consumption tubers as planting material is very high. Efforts are needed to replace consumption tuber planting material with other planting materials such as aerial tubers. This study examines the potential yield of aerial tuber planting material and consumption tuber with fertilization treatment. The study used a Completely Randomized Block Design with a split-plot pattern with two factors, namely: fertilizer (as the main plot) with two levels, namely: with liquid and chemical fertilizer. Types of planting material (as a subplot) with three levels, namely aerial tubers, large consumption tubers (1.93-2.05 cm) and small consumption tubers (1.04-1.29 cm). Repeat three times. Liquid organic fertilizer can be used to improve the chemical quality of the soil. The combination of bulbils planting material with chemical fertilizer resulted in the highest fresh weight of bulbs per plot and dry weight of bulbs per plot, namely 394.67 grams and 338.67 grams, respectively. However, the highest number of bulbs planted in the treatment of large consumption bulbs was 7 bulbs. The diameter of bulbs produced from bulbils planting material and bulb consumption was the same. Shallot bulbs have potential as planting material.
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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 | 0.001 | 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".