OPTIMALISASI PEMBERIAN BEBERAPA KONSENTRASI PUPUK ORGANIK CAIR (POC) JAKABA TERHADAP PERTUMBUHAN BIBIT KELAPA SAWIT (Elaeis guinensis Jacq.)
Bibliographic record
Abstract
Tujuan penelitian ini adalah untuk mendapatkan kosentrasi pupuk organik cair Jakaba yang optimal dalam meningkatkan pertumbuhan bibit kelapa sawit. Penelitian dilaksanakan sejak bulan Januari sampai bulan Maret 2023 di Kebun percobaan Fakultas pertanian Universitas Muhammadiyah Sumatera Barat. Rancangan penelitian adalah Rancangan Acak Lengkap (RAL) dengan 5 perlakuan 4 kelompok. Data pengamatan dianalisis menggunakan uji F yang dilanjutkan dengan uji Duncan’s New Multiple Range Test (DNMRT) pada taraf nyata 5% dengan perlakuannya adalah beberapa kosentrasi pupuk organik cair (POC) Jakaba 0 ml/L air, 150 ml/L air, 300 ml/L air, 450 ml/L air dan 600 ml/L air. Variabel pengamatan adalah tinggi tanaman, jumlah daun, Panjang daun terpanjang, lebar daun terlebar, diameter batang, berat basah dan berat kering bibit sawit. Hasil penelitian didapatkan konsentrasi pupuk organik cair (POC) Jakaba 450 ml/L air mampu meningkatkan pertumbuhan bibit kelapa sawit. Kata kunci : kosentrasi pupuk organik cair Jakaba, bibit kelapa sawit, pertumbuhan
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".