KOMBINASI FERMENTASI TEPUNG PUTAK DAN TEPUNG DAUN LAMTORO DALAM PAKAN TERHADAP PERTUMBUHAN DAN KELANGSUNGAN HIDUP IKAN BANDENG (Chanos chanos)
Bibliographic record
Abstract
Penelitian bertujuan mengkaji pertumbuhan dan kelulushidupan ikan bandeng perlakukan kombinasi tepung putak hasil fermentasi dan tepung daun lamtoro hasil difermentasi. Penelitian ini dilakukan menggunakan metode eksperimen dan perancangannya menggunakan Rancangan Acak Lengkap (RAL) dengan lima perlakuan dan tiga ulangan. Perlakuan yang dicobakan adalah A: fermentasi Tepung putak 25 % dan fermentasi tepung daun lamtoro 75 %; Perlakuan B: fermentasi tepung putak 50 % dan fermentasi tepung daun lamtoro 50 %; Perlakuan C: fermentasi tepung putak 75 % dan fermentasi tepung daun lamtoro 25 %; Perlakuan D: fermentasi tepung putak 100 %; perlakuan E: fermentasi tepung daun lamtoro 100 %. Perlakuan terbaik adalah perlakuan penggunaan kombinasi fermentasi tepung putak 75% dan fermentasi tepung daun lamtoro 25% dalam pakan yang memberikan pertambahan bobot mutlak terbesar yaitu 15,96 g, pertumbuhan spesifik harian 1,83 g%/hari dan kelangsungan hidup 100%.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".