Penanda Karakter Varitas Rambutan (Nephelium lappaceum L.) Berdasarkan Karakter Morfologi
Bibliographic record
Abstract
Abstrak. Rambutan merupakan buah eksotik yang banyak dibudidayakan di Indonesia. Rambutan mudah melakukan penyerbukan silang sehingga mengakibatkan tingginya kemungkinan munculnya varitas baru dan semakin sulit untuk dibedakan. Penelitian ini merupakan penelitian deskriptif kuantitatif dengan tujuan untuk menentukan penanda karakter morfologi rambutan. Prosedur penelitian meliputi tahap eksplorasi dan koleksi, pengamatan dan pengukuran, serta analisis penanda karakter. Eksplorasi dan koleksi dilakukan di Kecamatan Cileungsi dan Desa Bojong Kulur, Kabupaten Bogor, Jawa Barat. Sampel penelitian berupa ranting dengan daun dan buah dari tujuh varitas rambutan meliputi Sikoneng, Binjai, Aceh Lebak, Simacan, Sinyonya, Kerikil, dan Gula Batu. Bukti morfologi berupa 6 karakter kuantitatif dan 24 karakter kualitatif. Sebanyak 30 karakter yang dianalisis dengan Principal Component Analysis untuk menentukan karakter penanda tujuh varitas rambutan. Karakter penanda yang ditentukan digunakan sebagai kunci identifikasi. Hasil analisis menunjukkan bahwa karakter yang dapat digunakan antara lain: permukaan tangkai daun, bentuk ujung lamina, banyak buah pertandan, bentuk buah, warna kulit buah, berat kulit buah, kerataan warna kulit buah, ketertarikan buah, kerapatan rambut buah, warna rambut buah, warna aril, tekstur aril, kandungan air aril, aroma aril, kelekatan aril dengan kulit ari biji, kelekatan kulit ari biji dengan biji dan bentuk biji.
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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 | 0.000 |
| 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".