Sustainable cassava commodity agribusiness system in East Lampung Regency
Bibliographic record
Abstract
Introduction: The agribusiness system is a series of sustainable business management from upstream to downstream. Cassava farming is one of the strategic food crops that supports the national economy. Increasing cassava commodities cannot be separated from implementing an effective and efficient agribusiness system. The research aims to examine the sustainable cassava commodity agribusiness system in East Lampung Regency. Methods: The total population of cassava farmers in East Lampung Regency is 478 farmers. The sampling technique refers to the Sugiarto formula so 48 cassava farmers were obtained using a simple random sampling technique. Data analysis is 1) procurement of production facilities using a Likert scale, 2) farming using income analysis, 3) processing using added value, 4) marketing using marketing channels, marketing margins, and Farmer Share, and 5) Supporting institutional services. Results: Research results 1) Procurement of production facilities are in the category of being used. 2) cassava farming is profitable 3) processing cassava into tiwul products provides added value. 4) marketing of fresh and processed cassava is included in efficient marketing, and 5) supporting institutions have not contributed to cassava commodities. Conclusion: The cassava commodity agribusiness system has been established but the supporting services are not yet running well, so it is necessary to develop integrated agribusiness institutions.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".