Bibliographic record
Abstract
Artikel ini merupakan riset kualitatif yang bertujuan untuk mengkaji implementasi solusi pengelolaan kelapa sawit yang ramah lingkungan tetapi tetap menguntungkan secara ekonomis di Indonesia. Pengumpulan data dilakukan dengan studi literatur, yang diawali dengan mengidentifikasi permasalahan tata kelola perkebunan kelapa sawit kemudian solusi-solusi yang ditawarkan pemerintah. Hasil menunjukkan bahwa sertifikasi yang ditawarkan ternyata tidak dapat diterapkan terutama pada tipe perkebunan kepala sawit monokultur milik rakyat karena beberapa alasan yang membuka ruang untuk meninjau kembali solusi yang telah ada. Kami menyarankan pemerintah untuk membuat tipologi perkebunan kelapa sawit, mengevaluasi solusi-solusi yang ada dan menentukan mana yang terbaik untuk tiap-tiap tipologi tersebut.
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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; both teacher heads agree on what is shown here.
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".