IDENTIFIKASI PENYAKIT BERCAK DAUN NANAS DI KUBU RAYA, KALIMANTAN BARAT
Bibliographic record
Abstract
Permintaan akan buah nanas di dalam negeri terus meningkat, seiring dengan pertambahan jumlah penduduk dan meningkatnya kesadaran masyarakat terhadap manfaat vitamin yang terkandung di dalamnya. Namun demikian, keberadaan patogen menjadi kendala karena dapat menurunkan produksi tanaman nanas. Berbagai gejala bercak pada daun tampak di lapangan, yang mengindikasikan adanya serangan patogen. Penelitian ini bertujuan untuk mengidentifikasi gejala dan patogen penyebab penyakit bercak daun pada tanaman nanas serta menganalisis faktor-faktor yang mempengaruhi penyebarannya, guna memberikan dasar informasi bagi pengendalian penyakit yang lebih efektif. Penelitian dilaksanakan melalui sejumlah tahapan, seperti survei, evaluasi hasil survei, pengamatan gejala di lapangan, dan pengujian laboratorium terhadap patogen penyebab bercak daun. Sampel diambil secara purposive sampling dari tanaman nanas yang menunjukkan gejala bercak daun di Kabupaten Kubu Raya, Kalimantan Barat. Hasil penelitian menunjukkan bahwa tanaman nanas di wilayah tersebut terserang penyakit bercak daun dengan gejala berupa bercak berbentuk bulat dan teratur. Berdasarkan gejala tersebut, ditemukan keberadaan tiga jenis patogen, yaitu Curvularia sp., Fusarium sp., dan Discosia sp., yang diduga menjadi penyebab utama penyakit bercak daun pada tanaman nanas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".