KEANEKARAGAMAN HAMA LALAT BUAH PADA TANAMAN SAYURAN BUAH DI KABUPATEN BANGKA DAN KUNCI IDENTIFIKASINYA
Bibliographic record
Abstract
Lalat buah (Diptera: Tephritidae) merupakan hama penting bersifat invasif yang menyerang pada komoditas tanaman hortikultura khususnya tanaman buah dan sayuran buah. Informasi terkait jenis-jenis lalat buah perlu dilaporkan sebagai antisipasi dalam upaya pengendalian hama lalat buah. Pengoleksian lalat buah selain menggunakan atraktan juga dapat dilakukan dengan menggunakan host rearing. Penelitian ini bertujuan untuk mengetahui distribusi dan jenis spesies lalat buah apa saja yang terdapat pada tanaman sayuran buah di Kabupaten Bangka. Penelitian dirancang dengan menggunakan metode survei yang dilakukan disetiap kecamatan dan sampel penelitian diambil secara purposive sampling. Sampel yang menjadi target yang terindikasi lalat buah diambil dan kemudian di rearing. Lalat buah kemudian diidentifikasi dan dibuatkan kunci dikotomus. Hasil penelitian menunjukkan terdapat 10 jenis tanaman inang lalat buah antara lain cabai besar Capsicum annuum, cabai rawit Capsicum frutescens, mentimun Cucumis sativus, labu kuning Cucurbita moschata, melinjo Gnetum gnemon, oyong Luffa acutangula, paria Momordica charantia, tomat Solanum lycopersicum, terung Solanum melongena, and kacang panjang Vigna unguiculata. Lalat buah yang terkoleksi dalam penelitian ini sebanyak 6 spesies yaitu Bactrocera dorsalis, B.carambolae, B.mcgregori, Zeugodacus cucurbitae, Z. sp 1 dan Z. sp 2, dengan jumlah sebanyak 1.113 individu. Spesies yang paling dominan yaitu Z. cucurbitae pada tanaman sayuran buah di Kabupaten Bangka. Keanekaragaman dan kekayaan pada tanaman sayuran buah di Kabupaten Bangka dikategorikan rendah.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".