Commercialize the Cultivation of Yellow Pumpkin Plants
Bibliographic record
Abstract
This paper aims to explain the highly prospect and opportunity of yellow pumpkin commercialized and its indicate variable behind the lows interest of making it as the main crop by farming society. Cultivation technical approach used in identifying cultivating system of yellow pumpkin and use SWOT analysis to develop the strategy. Analyzing business feasibility commercialization of the cultivation of yellow pumpkin income calculations every harvest based on the primary data by the census of 9 sample group of farmers, observation, and in-depth interviews some key informants. The result of this research show that cultivation system of yellow pumpkin in Majasem Village do potential for cultivated commercially based on the mountain areas the mount of Northern Lawu with a production capacity 180.000 tons. Majasem Village qualified planting cultivation yellow pumpkin although using the simple and planting patterns the midst of rice, corn and soybean. Potential development cultivation yellow pumpkin in the future identified based on internal and external factors have a great capital opportunity to commercialization in supporting industrialization food and drink. Business feasibility in commercialization by requiring investment IDR 3.579.800 and IDR 6.211.667 at operational cost can produce income IDR 8.550.000 per harvest or IDR 2.338.333 per month with long turning capital during 2 months.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".