Pengaruh Plant Growth Promoting Rhizobacteria Terhadap Bibit dan Pertumbuhan Awal Pepaya
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui pengaruh PGPR pada bibit dan pertumbuhan awal pepaya. Percobaan dilakukan dari bulan Februari sampai Mei 2015 di Kebun Percobaan Pusat Kajian Hortikultura Tropika IPB Pasirkuda Ciomas, Bogor, dengan rancangan kelompok lengkap teracak 2 faktor dan 5 ulangan. Perlakuan adalah konsentrasi larutan PGPR (5 ml L-1, 10 ml L-1 dan 15 ml L-1) dan lama perendaman PGPR (30 menit, 60 menit,90 menit dan 120 menit). Data yang diperoleh dianalisis dengan uji F dan perlakuan berpengaruh dianalisis dengan DMRT (Duncan Multiple Range Test) pada taraf selang kepercayaan 5%. Hasil penelitian di polybag menunjukkan bahwa konsentrasi larutan PGPR, lama perendaman PGPR dan interaksi antara konsentrasi PGPR dengan lama perendaman PGPR mempengaruhi jumlah daun dan diameter batang di fase pembibitan. Hasil penelitian di lapangan menunjukkan bahwa konsentrasi larutan PGPR, lama perendaman PGPR dan interaksi antara konsentrasi PGPR dengan lama perendaman PGPR tidak mempengaruhi tinggi tanaman, jumlah daun, panjang petiol, lebar daun, panjang daun, waktu bunga pertama muncul, tinggi kedudukkan bunga, jumlah pohon betina, jumlah pohon hermaprodit, jumlah bunga betina dan jumlah bunga hermaprodit. Konsentrasi PGPR mempengaruhi panjang petiol pada 5 minggu setelah tanam.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".