Analisis Kontribusi Usaha Ternak Domba Terhadap Pendapatan Peternak di Kecamatan Kertajati Kabupaten Majalengka
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 research aims to determine the analysis of household income, the contribution of sheep farming to farmer income, and the factors that influence the size of the contribution of sheep farming. This research was carried out from June to August 2022. The analytical methods used were descriptive analysis, contribution analysis and multiple linear regression analysis. Respondents were determined using a purposive sampling method of 85 respondents. The variables observed were the number of working hours, number of workers, and number of livestock. The research results show that the average income of sheep farming is IDR 2.289.513/year. Meanwhile, the average income from breeders' business is IDR 7.730.746 /year. The sheep farming business contributes 29.61% to the total income of farmers in Kertajati District, Majalengka Regency. The coefficient of determination (R2) was obtained at 0.039. which means that the number of livestock, the amount of working hours, and the number of workers influence the contribution of sheep livestock by 3.9%, so Y is influenced by 96.1% by other factors outside the independent variable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it