Pengaruh Pemberian Ekstrak Daun Mangrove Rizhophora Apiculata terhadap Performa Pertumbuhan Udang Vaname
Bibliographic record
Abstract
Kendala yang sering dihadapi oleh para pembudidaya adalah masalah hama dan penyakit yang dapat menurunkan kualitas udang dan kegagalan produksi. Penyakit yang sering menyerang udang adalah penyakit bakterial seperti vibriosis. Penyakit bakterial biasanya ditangani menggunkan antibiotik. Pada penelitian ini antibiotik yang digunakan berasal dari ekstrak daun mangrove Rhizophora apiculata yang memiliki kandungan senyawa aktif sebagai antibakteri. Penggunaan ekstrak daun mangrove R. apiculata diharapkan mampu meningkatkan performa pertumbuhan udang vaname melalui peningkatan sistem imun. Penelitian ini bertujuan untuk mengetahui pengaruh ekstrak daun mangrove R. apiculata terhadap performa pertumbuhan udang vaname (Litopenaeus vannamei). Penelitian ini dilakukan selama 40 hari. Perlakuan yang diberikan yaitu penambahan ekstrak daun mangrove R. apiculata ke dalam pakan udang yaitu P1 kontrol tanpa penambahan ekstrak, P2 diberikan ekstrak mangrove 0,5%, P3 diberikan ekstrak mangrove 1% dan P4 diberikan ekstrak mangrove 2%. Hasil yang diperoleh untuk performa pertumbuhan udang vaname perlakuan terbaik pada P4 (pakan + ekstrak mangrove 2%) dengan hasil pertumbuhan bobot mutlak sebesar 17,41 g, pertumbuhan spesifik sebesar 21,37%, kelangsungan hidup sebesar 93% dan konversi pakan sebesar 1,1. Daun mangrove R. apiculata dapat meningkatkan performa pertumbuhan udang vaname melalui pertumbuhan bobot mutlak, laju pertumbuhan spesifik, kelangsungan hidup dan nilai rasio konversi pakan, nilai terbaik diperoleh pada dosis ekstrak 2% dengan masing-masing nilai 17,31 g, 21,37%, 93% dan 1,1%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".