Community Assessment of Flood Risks and Early Warning System in Ratu Watershed, Koshi Basin, Nepal
Bibliographic record
Abstract
Nepal is highly vulnerable to flood-related disasters which cause considerable loss of lives and property. The vulnerability of communities to flood-related hazards can be reduced by proper planning, preparedness, and responses using various structural and nonstructural measures. The community-based flood early warning system is one such tool that enables local communities to enhance their resilience to flooding risks. This paper highlights the efficacy of the community assessment of flood risks and early warning systems. Using qualitative and quantitative methods, this paper evaluates the progress of a community-based flood early warning system implemented in the Ratu River—a small tributary of the Koshi River. The establishment of a community network in 2015 was instrumental in the dissemination of flood early warning information and in building local capacities to understand the risks and take timely action. The flood early warning resulted in awareness-raising, strengthened upstream–downstream linkages, and resulted in a greater willingness among communities to help each other prepare for flood disasters in the Ratu watershed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".