KAJIAN PERUBAHAN IKLIM DI DKI JAKARTA BERDASARKAN DATA CURAH HUJAN
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
A common annual problem that often occurs in DKI Jakarta is flooding. Extreme rainfall is one of the most dominant factors that trigger flooding in DKI Jakarta. Global warming causes climate change and rainfall characteristics. This study aims to understand the characteristics of the climate rainfall in DKI Jakarta at this time and the potential for changes in the future. In this study, the characteristics of rainfall which is analyzed were rainfall variabilities such as annual rainfall, maximum rainfall, and the number of rainy days as indicated by analysis of rainfall trends or the tendency of changes in rainfall characteristics over time. Rainfall prediction simulation is carried out using the Statistical Downscaling method. The climate model used is CanESM5 (The Canadian Earth System Model version 5), which is one of the climate models in the Assessment Report (AR6) issued by the IPCC in 2022. The future rainfall at each station is projected for the future period (FP), namely FP-1 (2025-2049), FP-2 (2050-2074), and FP-3 (2075-2100) with the climate scenario Shared Socio-economic Pathways (SSP) 3-7,0. Predictive rainfall analysis yields information that the average annual rainfall, average maximum rainfall and the number of rainy days generally increase in each future period when compared to the historical annual average rainfall. In general, climate change does not result in changes in monsoon rainfall patterns. However, global warming has the potential to increase future rainfall and speed up the start of the rainy season.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it