KAJIAN PERUBAHAN IKLIM DI DKI JAKARTA BERDASARKAN DATA CURAH HUJAN
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.014 |
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".