EFESIENSI TEKNIS USAHATANI KOPI ROBUSTA DI KABUPATEN LIMA PULUH KOTA PROVINSI SUMATERA BARAT
Bibliographic record
Abstract
Kabupaten Lima Puluh Kota merupakan salah satu daerah yang mempunyai potensi dalam pengembangan tanaman kopi Robusta. Faktor produksi yang tersedia belum dapat menjamin tingginya produktivitas. Tujuan penelitian ini adalah menganalisis faktor-faktor yang mempengaruhi produktivitas kopi Robusta, menganalisis tingkat efisiensi teknis usahatani kopi Robusta dan menganalisis faktor-faktor yang mempengaruhi efisiensi teknis usahatani kopi Robusta di Kabupaten Lima Puluh Kota. Metode yang digunakan dalam penelitian ini adalah metode survei pada 60 orang sampel melalui pengambilan sampel secara acak sederhana. Analisis data menggunakan fungsi produksi stochastic frontier Cobb-Douglas. Hasil penelitian menunjukkan Faktor-faktor yang berpengaruh secara signifikan terhadap produktivitas kopi Robusta di Kabupaten Lima Puluh Kota adalah umur tanaman dan jumlah pohon. Faktor umur tanaman dan jumlah pohon berdampak positif terhadap produktivitas kopi. Tingkat efisiensi teknis usahatani kopi di Kabupaten Lima Puluh Kota mulai dari 0,54 sampai 0,99 dengan rata-rata tingkat efisiensi teknis adalah 0,83. Artinya petani sudah efisien secara teknis namun masih bisa meningkatkan produktivitas kopi.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".