OPTIMALISASI PEMBERIAN BEBERAPA KONSENTRASI PUPUK ORGANIK CAIR (POC) JAKABA TERHADAP PERTUMBUHAN BIBIT KELAPA SAWIT (Elaeis guinensis Jacq.)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.010 |
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 it