Perbedaan Umur Panen Terhadap Pertumbuhan dan Kandungan Karaginan Rumput Laut Kappaphycus alvarezii Yang Terkena Ice Ice
Bibliographic record
Abstract
Rumput laut tidak memiliki akar, batang, dan daun yang sejati akan tetapi keseluruhan rumput laut disebut talus. Salah satu produk yang dihasilkan dari rumput laut K. alvarezii adalah karaginan. Karaginan merupakan senyawa polisakarida dan dimanfaatkan dalam bidang industri, pangan dan farmasi. Penelitian ini bertujuan untuk mengkaji studi pertumbuhan dan kandungan karaginan dari rumput laut K. alvarezii pada yang terkena penyakit ice-ice. Penelitian ini telah dilaksanakan selama 45 hari mulai dari bulan juli sampai bulan September 2021 di perairan Pasir Panjang, Kota Kupang. Rumput laut dibudidayakan menggunakan metode long line. Pengujian Kandungan Karaginan dilakukan di Laboratorium Fakultas Kelautan dan Perikanan, Universitas Nusa Cendana. Penelitian ini menggunakan Rancangan Acak Lengkap (RAL) dengan empat perlakuan dengan setiap umur panen berbeda yaitu umur panen 15 hari, 25 hari, 35 hari, dan 45 hari. Hasil penelitian kandungan karaginan tertinggi pada umur panen 45 hari 33,3%. Hasil ANOVA menunjukkan perlakuan umur panen berbeda memberikan pengaruh yang signifikan terhadap kandungan karaginan rumput laut K. alvarezii. Kata kunci : K. alvarezii, Pertumbuhan, Karaginan, Ice- ice
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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 | 0.001 | 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) | 0.010 | 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".