KONTRIBUSI USAHATANI KELAPA TERHADAP PENDAPATAN KELUARGA DI DESA KLABAT KECAMATAN DIMEMBE KABUPATEN MINAHASA UTARA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aims to find out: 1) The amount of average income of coconut farmers per quarter, 2) The amount of contribution of coconut farming to family income per quarter. Data collection in this study was conducted from October to November 2018 in Klabat Village, Dimembe District, North Minahasa Regency. The method used is the survey method, using primary data and secondary data. Primary data was obtained through direct interviews with 25 coconut farmers and one person from the Klabat Village based on a list of questions that had been prepared previously. Secondary data in this study were sourced from local bookstores, and the internet through Google Scholar to access articles from various scientificjournals and theses from Sam Ratulangi University and other universities related to the contribution of coconut farming to family income. The data obtained were analyzed using contribution analysis and using descriptive analysis presented in table form. The results showed that the amount of income received by coconut farmers was Rp. 1,837,320. While the contribution of coconut farming to household income is 27.45%. This means that coconut farming provides a moderate contribution and cannot be used as the main source of household income in Klabat Village.*eprm*
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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 it