APLIKASI HISTOGRAM UNTUK ANALISIS VARIABILITAS TEMPORAL DAN SPASIAL HUJAN BULANAN: STUDI DI WILAYAH UPT PSDA DI PASURUAN JAWA TIMUR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Penelitian ini bertujuan untuk menganalisis variabilitas hujan bulanan di wilayah UPT PSDA di Pasuruan. Wilayah studi mencakup kabupaten Probolinggo, Kota Probolinggo, Kabupaten Pasuruan dan Kota Pasuruan di Jawa Timur. Data hujan harian dari 93 stasiun, dengan panjang rekaman data dari tahun 1980 sampai dengan 2015 digunakan sebagai input utama. Tahap penelitian mencakup: (1) pra-pengolahan data, (2) analisis variabilitas temporal, (3) Analisis variabilitas spasial, (4) interpolasi dan pembuatan peta tematik dan (5) interpretasi. Data hujan bulanan diperoleh dari penjumlahan hujan harian. Pra-pengolahan data dilakukan menggunakan excel. Data hujan bulanan ditabulasi selama 35 tahun periode rekaman data. Selanjutnya, metode interpolasi IDW digunakan untuk membuat berbagai peta tematik hujan. Penelitian ini menghasilkan deskripsi variabilitas spasial dan temporal hujan per sub-wilayah dan berbagai peta tematik terkait dengan karakteristik spasial hujan di wilayah tersebut. Hujan bulanan rerata di wilayah tersebut 152 mm/bulan. Hujan bulanan maksimum 798 mm per bulan.
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.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it