PENENTUAN INDEKS BAHAYA KEKERINGAN AGRO-HIDROLOGI: STUDI KASUS WILAYAH SUNGAI KARIANGO SULAWESI SELATAN
Bibliographic record
Abstract
Kekeringan agro-hidrologi dapat diartikan sebagai kekurangan air permukaan, air tanah dan mencukupi untuk tanaman dan kebutuhan masyarakat untuk jangka waktu tertentu. Sejauh ini belum ada indeks kekerigan agro-hidrologi yang menggabungkan faktor iklim, air permukaan, dan air bawah permukaan tanah. Penelitian ini merumuskan sebuah indeks bahaya kekeringan (Ibk) sebagai indikator kekeringan agro-hidrologi. Model yang dikembangkan dari kombinasi curah hujan musim kering, kedalaman air tanah, jarak sumber air, tekstur tanah dan indeks ketersediaan air bagi tanaman dengan menggunakan metode penginderaan jauh dan GIS. Indeks bahaya kekeringan agro-hidrologi yang telah dikembangkan adalah Ibk= (0.33CH) + (0.27KAT) + (0.20SA) + (0.13T) + (0.0WSVI) dengan hasil validasi model menunjukkan kemiripan yang tinggi kekeringan di lapangan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".