Perbaikan Teknik Pembrongsongan melalui Aplikasi Pestisida untuk Meningkatkan Kemulusan Buah Jambu Kristal (Psidium guajava L)
Bibliographic record
Abstract
Jambu ‘kristal’ merupakan kultivar unggulan jambu biji dan memiliki pasar yang baik di Indonesia. Jambu kristal memiliki rasa yang manis, tekstur renyah, vitamin C, dan kandungan lain yang bermanfaat. Kualitas merupakan masalah utama dalam budidaya jambu ‘kristal’, salah satunya adalah tingkat kemulusan buah. Penelitian ini bertujuan mengetahui pengaruh bahan aktif pestisida yang digunakan pada teknik pembrongsongan buah terhadap tingkat kemulusan buah. Penelitian dilaksanakan di Kebun Percobaan Cikabayan dan Laboratorium Pascapanen, Departemen Agronomi dan Hortikultura, Institut Pertanian Bogor pada Bulan Februari 2017 hingga Agustus 2017. Bahan yang digunakan pada teknik pembrongsongan adalah bahan aktif pestisida: Klorpirifos, Abamektin, dan Mankozeb. Hasil penelitian menunjukkan bahwa perlakuan bahan aktif pestisida memberikan pengaruh yang sangat nyata pada peubah kemulusan buah. Perlakuan bahan aktif pestisida meningkatkan kemulusan buah hingga dua kali lipat. Perlakuan bahan aktif pestisida tidak memberikan pengaruh nyata pada peubah diameter, kelunakan, bobot, PTT, dan ATT.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.013 |
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".