Vermicompost Formulation Based on Soybean Husk and Cow Manure on Shallots
Bibliographic record
Abstract
Shallot is one of an important vegetable in Indonesia. The yield of the crop is often constrained by low and unbalanced nutrient supply in the soil. The application of vermicompost based on soybean husk and cow manure can increased nutrient supply in the soil. This research aimed to obtain the optimum vermicompost formulation and doses based on soybean husk and cow manure on shallots. This research is a field that arranged Randomized Complete Block Design (RCBD) with 1 factor (vermicompost formulation (V) that consisted 4 doses for each treatment and 3 replication was applied. The treatment consisted of vermicompost formulation (V): 100% soybean husk, 100% cow manure, 50% soybean husk: 50%: cow manure, 75% soybean husk: 25%: cow manure and 25% soybean husk: 75%: cow manure. Each treatment consisted of four doses: without application, 5, 10, 15 t. ha-1, so the total treatment was 20 level. Data were analyzed using the least significant difference (LSD) test. The result indicated that vermicompost formulation gave significant effect on all of observation parameters on the growth and yield. The lowest response of shallots occurred in the treatments without vermicompost application on all formulations, and the highest was in the 100% soybean husk formulation at a dose of 15 t. ha-1.
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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 | 0.000 |
| 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".