Bibliographic record
Abstract
IntisariBanjir besar kembali melanda ibukota Jakarta pada tanggal 17 Januari 2013. Hujan yang deras sejak tanggal 12 Januari 2013 di wilayah Jabodetabek menyebabkan banjir kembali melanda wilayah Jakarta. Banyaknya genangan juga menimbulkan kemacetan yang luar biasa yang kemudian menyebabkan lumpuhnya aktifitas ekonomi. Banjir ini disebut-sebut sebagai yang terburuk setelah banjir tahun 2007. BMKG melaporkan bahwa hujan ekstrim terjadi pada tanggal 17 dan 18 Januari, dan hal ini juga terpantau oleh TRMM yang mencatat bahwa hujan terjadi terus-menerus dengan curah hujan yang tinggi sejak tanggal 12. Analisis data radar menunjukkan bahwa pada tanggal 17, hampir seluruh wilayah Jakarta ditutupi oleh awan hujan yang tebal. Awan-awan hujan yang muncul mencapai ketinggian lebih dari 7 km dan masuk ke Jakarta dari arah Barat Laut. Pada tanggal 17, hampir seluruh awan hujan yang muncul mempunyai ketebalan lebih dari 7 km. AbstractHeavy flood has been hit Jakarta on January 17 , 2013. Heavy rains from January 12, 2013 in the Greater Jakarta area causing floods, which is said as the worst since 2007. BMKG reported that extreme rainfall occurred on 17 and 18 January, and it is also observed by TRMM which noted that rain occurs continuously with high rainfall since the Jan 12th. Radar data analysis showed that on the 17th, almost the entire area of Jakarta covered by thick towering precipitation clouds. These clouds appeared more than 7km height and move Westward - Northwestward . On the 17th, almost all the rain clouds that appear to have a thickness of more than 7km .
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".