Efektivitas Pemberian ZPT Bawang Merah (Allium Cepa L.) terhadap Subkultur Tanaman Pisang Barangan (Musa Acuminata L.) secara In Vitro
Bibliographic record
Abstract
Perbanyakan tanaman umumnya dilakukan secara generatif melalui biji, atau secara vegetatif melalui bagian tanaman. Namun, banyak tanaman yang sulit diperbanyak secara generatif, sehingga populasinya terus berkurang. Subkultur menjadi salah satu cara perbanyakan tanaman dalam kultur jaringan dengan pemindahan planlet yang masih sangat kecil (planlet muda) dari medium lama ke medium baru secara aseptic. Bawang merah (Allium cepa L.) salah satu zat pengatur tumbuh alternatif yang di tambahkan pada media tanam kultur jaringan pisang barangan (Musa acuminata L.) yang dapat memberikan pertumbuhan dan morfogenesis yang lebih baik bagi planlet tanaman. Tujuan penelitian untuk mengkaji pertumbuhan pisang barangan dengan pemberian larutan bawang merah dan konsentrasi yang baik pada pertumbuhan pisang barangan. Penelitian ini menggunakan pendekatan kuantitatif. Teknik analisis data yang di peroleh dari hasil penelitian ini menggunakan uji anava. Hasil uji F pada analisis sidik ragam menunjukkan bahwa pemberian larutan bawang merah pada pisang barangan berpengaruh sangat nyata terhadap jumlah akar umur 28 dan 84 hari setalah tanam (HST). Pemberian larutan bawang merah memberikan pengaruh nyata pada jumlah akar pada konsentrasi BP4 (MS + larutan bawang merah (5 ml/l).
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".