Analisis Faktor-Faktor yang Mempengaruhi Pendapatan Petani Padi di Kecamatan Babirik Kabupaten Hulu Sungai Utara
Bibliographic record
Abstract
This research was designed to (1) calculate land productivity; (2) to analyze effect of the land area, labor, seeds and fertilizers on the income of rice farmers; (3) find out the most dominant factors affecting the income of rice farmers. This research took the case study area in Babirik Sub-district, HSU disctrict. The data source is primary, obtained via questionnaires will be analyzed with multiple regression linier which will be tested through the f-test and the t-test. This research found that land productivity was up to standard. Then simultaneously land area, labor, seeds and fertilizers had an significant effect. Land area is the most dominant factor affecting the income of rice farmers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".