ANALISIS PENDAPATAN DAN SISTEM PEMASARAN PADI ORGANIK DAN ANORGANIK DI KABUPATEN PRINGSEWU
Bibliographic record
Abstract
The aims of this research are to analyze income, comparison of income, and marketing effeciency of organic and inorganic rice farming in Pringsewu District. This research was conducted in Fajaresuk Village Pringsewu Subdistrict, Pringsewu District using a survey method Data were collected from August to September 2017. The sample size in research were 14 organic rice farmers, 25 inorganic rice farmers, 15 marketing respondents including 1 member of Sejahtera Farmer Group, 9 rice merchants, and 5 millers based on rice marketing flow (snowball). The data were analyzed using income, comparison of income, and marketing analyses. The results of study showed that organic rice farming income bigger than inorganic rice farming income. There is a significant difference between organic and inorganic rice farming income. The marketing of organic rice is more efficient than inorganic rice. Key words: income, inorganic rice, marketing, organic rice
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".