ANALISIS PENDAPATAN PETANI KELAPA DI DESA RANOKETANG ATAS KECAMATAN TOULUAAN KABUPATEN MINAHASA TENGGARA (Income Analysis of Coconut Farmers in Ranoketang Atas Village, Touluaan Sub District, Southeast Minahasa Regency)
Bibliographic record
Abstract
The objective of this research is to analyze income, income comparison and the feasibility of coconut farming, both those selling in the form of coconut seeds and those selling in the form of copra in Ranoketang Atas Village, Touluaan District, Southeast Minahasa Regency. Data collection methods are interviews, recording and documentation. The data collected in this study are primary data obtained from direct interviews with respondents through questionnaires and secondary data obtained from the Ranoketang Atas Village office and Touluaan Sub District Office. The research results indicated that there is a significant difference level of income between farmers who sell coconuts in the form of coconut seeds and farmers who sell coconuts in the form of copra with the average income of farmers who sell coconuts in the form of coconut seeds of Rp. 16,915,929 per quarter and the average income of farmers who sell coconut in the form of copra is Rp. 20,719,939 per quarter. The results of the feasibility analysis of farming obtained a value of more than 1 which means that coconut farming run by coconut farmers in the village of Ranoketang Atas, Touluaan Sub District, Southeast Minahasa Regency is feasible.
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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 | 0.001 | 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) | 0.005 | 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".