DAMPAK EL-NINO PADA PRODUKSI PADI (Oryza sativa) DI KOTA SERANG
Bibliographic record
Abstract
Abstrak El Nino berdampak pada penurunan curah hujan sehingga memicu kekeringan di wilayah Kota Serang. Kondisi kekeringan ini diikuti dengan penurunan produksi padi pada beberapa wilayah yang berpengaruh terhadap penurunan produksi padi Kota Serang tahun 2023. Kasemen dan Taktakan menjadi 2 (dua) wilayah dengan dampak kekeringan paling luas, namun Kecamatan Taktakan dan Walantaka justru menjadi penyumbang penurunan produksi Kota Serang yang cukup tinggi. Penurunan yang besar di Kecamatan Taktakan dan Walantaka dipengaruhi jenis lahan. Hampir 95,3 % wilayah Kecamatan T aktakan merupakan lahan tadah hujan, sementara Kecamatan Walantaka mengandalkan irigasi setengah teknis dan tadah hujan. Tahun 2023 merupakan tahun dengan produksi terendah dalam 4 (empat) tahun terakhir . Kata Kunci: el nino, kekeringan, pertanian, produksi padi A b stract El Nino has an impact on decreasing rainfall, thereby triggering drought in the Serang City area. This drought condition was followed by a decline in rice production in several areas which influenced the decline in overall rice production at The Serang City in 2023. Kasemen and Taktakan were the 2 (two) regions with the most widespread impact of the drought. However, Taktakan Tahun 2023 , menjadi tahun dengan produksi padi terendah selama kurun waktu 4 (empat) tahun and Walantaka Districts actually contributed to the quit hight decline in Serang City's production. The large decline in Taktakan and Walantaka Districts affected land types. Almost 95.3% of the Taktakan District area is rain-fed land, while Walantaka District relies on semi-technical and rain-fed irrigation. The year 2023 was the lowest rice production in past 4 (four) years. Keywords: el nino, drought , agriculture, rice production
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".