Ketangguhan Kota Semarang dalam Menghadapi Bencana Banjir Pasang Air Laut (Rob)
Bibliographic record
Abstract
<span lang="EN-US">Climate change becomes a big problem for Semarang City. One of the effects of climate change in Semarang City is the tidal flooding (rob). The existing tidal flooding is a consequence of the increase of land surface subsidence and the rise of sea level. It affects many aspects of life and livelihood of the society, including on physical, social, economic, environmental, and health aspects. In 2012, </span>MercyCorps and ARUP<span lang="EN-US"> declared Semarang City as one of the cities resilience to climate change. </span><span lang="EN-US">In 2014, Semarang City became the only city in Indonesia to take part in the 100 Resilient Cities program (100 RC). </span><span lang="EN-US">This research aims to identify the measures that have been done by stakeholders in facing tidal flood and to measure the resilience level of Semarang City in encountering tidal flood. The location of this research covers the area along the coast of Semarang City which stretches from west to east as far as 13.6 km. This research uses quantitative-qualitative deductive method. Based on the analysis result, the measures that have been done by the stakeholders in Semarang City resulted in “medium” resilience level against tidal flood. This implies that Semarang City has not yet been resilient in facing tidal flood. </span><span lang="EN-US">However, the existence of the 100RC program has made Semarang City Government work harder to achieve the goal of the 100RC project, which is Semarang City that is resilient in facing all challenges, both in the form of shock and stress.</span>
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".