UJI PERTUMBUHAN DAN HASIL ENAM VARIETAS TANAMAN TERUNG (Solanum melongena, L.) PADA TANAH PODSOLIK MERAH KUNING
Bibliographic record
Abstract
Produksi terung di Kabupaten Sintang masih rendah hanya 0,66 ton/Ha, penyebabnya karena beberapa faktor diantaranya adalah tanah yang kurang subur karena didominasi oleh tanah Podsolik Merah Kuning. Upaya meningkatkan produksi tanaman dapat dilakukan dengan beberapa langkah salah satunya adalah pemilihan varietas yang sesuai dengan kondisi lingkungan tumbuh terutama tanah dan iklim. Tujuan penelitian ini untuk mengetahui pertumbuhan dan hasil enam varietas terung pada tanah Podsolik Merah Kuning. Penelitian ini menggunakan metode percobaan lapangan dan menggunakan Rancangan Kelompok Lengkap Teracak (RKLT) faktor tunggal dengan empat ulangan. Perlakuan penelitian terdiri dari Varietas Terung Mustang (V1), Milano (V2), Bungo (V3), Lezata (V4), Turangga (V5) dan Raos (V6). Data hasil pengamatan dianalisis dengan uji F, jika menunjukkan ada pengaruh nyata pada selang kepercayaan 0,05 kemudian dilanjutkan dengan uji Duncan Multiple Range Test (DMRT). Hasil penelitian menunjukkan bahwa dari enam varietas tanaman terung dengan pertumbuhan dan hasil tertinggi adalah Lezata, rata-rata pertambahan tinggi tanaman 44,04 cm, persentase bunga menjadi buah (5,76%), jumlah buah (2,47 buah), dan berat buah tertinggi 20,71 g.
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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) | 0.000 | 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) | 0.005 | 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".