ESTIMASI OUTPUT BABI DI KABUPATEN TABANAN PROVINSI BALI
Bibliographic record
Abstract
Tujuan penelitian ini adalah untuk membuat estimasi output pada babi Bali dan babi Landrace di Kabupaten Tabanan. Penelitian ini dilakukan di lima kecamatan di Kabupaten Tabanan dengan mengambil 150 peternak. Data yang diambil adalah identitas peternak, komposisi, dan reproduksi ternak babi Bali dan babi Landrace. Estimasi output dihitung dengan cara pendekatan teori pemuliaan. Hasil dari penelitian ini diketahui bahwa nilai natural increase babi Bali dan babi Landrace sebesar 60,93 dan 116,38%. Nilai net replacement rate jantan dan betina babi Bali dan jantan dan betina babi Landrace sebesar 7.664,29 dan 1.844,05% serta 15.033,33 dan 1.386,47%. Nilai output pada babi Bali jantan dan betina sebesar 30,78 dan 23,58% atau 360 dan 114 ekor serta jantan dan betina pada babi Landrace sebesar 45,57 dan 70,49% atau 6.722 dan 10.009 ekor. Kesimpulan dari penelitian ini adalah estimasi output pada babi Bali jantan (30,78%) lebih besar daripada babi Bali betina (23,58%), sedangkan pada estimasi output babi Landrace jantan (45,57%) lebih kecil daripada babi Landrace betina (70,49%). (Kata kunci: Estimasi output, Babi Bali, Babi Landrace)
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".