Model Volume Pohon Sengon Untuk Menilai Kehilangan Keuntungan Petani Hutan Rakyat
Bibliographic record
Abstract
Sistem penjualan pohon sengon (Paraserianthes falcataria L.) di hutan rakyat tanpa menggunakan model volume pohon yang tepat dapat menimbulkan kehilangan keuntungan petani. Tujuan penelitian ini adalah untuk memperoleh model volume sengon dan mengevaluasi praktek yang biasa dilakukan dalam penjualan kayu sengon di hutan rakyat. Hasil penelitian menunjukkan bahwa model volume yang dapat digunakan secara akurat untuk memperkirakan volume pohon sengon di hutan rakyat dapat diduga dengan hanya mengukur diameter pohon. Sistem penjualan berdasarkan batangan dan luasan menyebabkan petani kehilangan keuntungan sebesar 24,99% dan 32,19%. Untuk menghindari kelemahan tersebut maka direkomendasikan model volume sebagai penduga volume pohon sengon dengan akurat.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".