Community preparedness toward flood during Covid-19 pandemic at Pekalongan City and Regency
Bibliographic record
Abstract
Several countries experience difficulties in overcoming the effects of natural disasters amid the Covid-19 pandemic, such as Typhoon Hagibis in Japan, floods due to melting snow in Canada, Typhoons in Bangladesh, and Cyclone Harold in Pacific countries. Natural disasters that affected the world during infectious diseases did not only occur in 2020. Earthquakes struck Haiti during the 2010 Cholera epidemic outbreak and respiratory infections during the Great East Japan Earthquake and Tsunami in 2011. Something similar happens in Indonesia, one of which is flood and tidal flood in Pekalongan that occur during the Covid-19 pandemic. This study reviews the efforts of countries in overcoming natural disasters during the pandemic. It aims to propose an approach for flood disasters preparedness in Pekalongan so that disaster preparedness process including victim evacuation, can be done without increasing the spread of Covid-19. Information about humanity, disaster management, health, water and sanitation that are disseminated to the public must be supported by scientific knowledge to avoid the spread of myths and negative stigma. Coordination between stakeholders and the local community plays the most important role in flood disaster preparedness with the Covid-19 protocol during the pandemic.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".