Diagnosis of Corm Rot Disease of Taro: Case Study at Maybrat - West Papua
Bibliographic record
Abstract
Talas (Colocasia esculenta L. Schott) merupakan tanaman herba yang telah dimanfaatkan sebagai bahan pangan di beberapa wilayah di dunia. Tanaman ini dipercaya berasal dari Asia Tenggara (Lebot et al. 2010). Di Maybrat, Papua Barat talas telah dimanfaatkan sebagai tanaman bahan pangan pokok utama sejak dahulu oleh masyrakat lokal dan disebut dengan nama ‘Wiah atau Awiah’. Hingga awal tahun 2013, belum terdapat laporan yang memadai terkait serangan hama dan penyakit tanaman talas di daerah ini. Namun, pada pertengahan tahun 2013 petani setempat melaporkan adanya epidemi penyakit busuk umbi talas di pertanamannya. Tujuan penelitian ini ialah untuk membuktikan penyebab penyakit busuk umbi talas di Maybrat. Pengujian dilakukan secara in vivo dan in planta pada umbi dan tanaman talas. Dari 38 isolat cendawan yang diisolasi, 50% bersifat patogen. Tiga isolat yang terpilih (Y1, YP1, dan S4) mampu menyebabkan pembusukan jaringan umbi secara in vivo dan kerusakan akar secara in planta. Uji in planta menunjukkan bahwa inokulasi gabungan dua isolat dari ketiganya mampu menyebabkan kerusakan akar yang berat. Secara morfologi, isolat YP1 dan S4 diidentifikasi sebagai Fusarium oxysporum dan Y1 merupakan F. solani. Ini merupakan laporan yang pertama tentang penyakit busuk umbi talas dari Maybrat, Papua Barat.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".