The Respon Growth and Yield of Shallots (Allium ascalonicum L.) on Dosage Cow Manure and Rice Husk
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui pengaruh dosis pupuk kandang kotoran sapi dan sekam padi terhadap pertumbuhan dan hasil tanaman bawang merah (Allium ascalonicum L.). Penelitian dilaksanakan pada bulan Februari sampai dengan Juni, di Desa Kungkai Baru, Kecamatan Air Periukan, Kabupaten Seluma. Percobaan menggunakan Rancangan Acak Kelompok Lengkap faktorial dengan 2 faktor dan 3 ulangan. Faktor pertama adalah dosis pupuk kandang kotoran sapi, terdiri dari tiga taraf, yaitu 10 ton/ha, 20 ton/ha dan 30 ton/ha. Faktor kedua adalah dosis sekam padi, terdiri dari tiga taraf, yaitu 10 ton/ha, 20 ton/ha dan 30 ton/ha . Data dianalisis dengan analisis ragam dan dilanjutkan dengan uji Duncan’s Multiple Range Test pada taraf 5%. Hasil penelitian menyimpulkan bahwa perlakuan dosis sekam padi berpengaruh nyata terhadap tinggi tanaman 2 MST dan 4 MST, serta jumlah daun 2 MST. Perlakuan dosis pupuk kandang kotoran sapi berpengaruh tidak nyata terhadap semua peubah pengamatan. Hasil penelitian menunjukkan bahwa interaksi dosis pupuk kandang kotoran sapi dan sekam padi berpengaruh nyata terhadap jumlah umbi per rumpun, bobot umbi segar, dan produksi per petak. Perlakuan pupuk kandang kotoran sapi 10 ton/ha dan sekam padi 30 ton/ha terbaik dan memberikan hasil tanaman bawang merah tertinggi, yaitu jumlah umbi per rumpun 4,25 siung, bobot umbi segar 36,25 g, dan produksi per petak 237,19 g/m2.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 0.000 |
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".