Vulnerability measures for flood and drought and the application in hydrometric network design
Bibliographic record
Abstract
Climatic variability and change can have profound impacts on human societies and wildlife habitats. Extreme events and natural hazards such as floods, droughts, and windstorms, can lead to loss of lives, economic damages, and disruption in livelihoods, infrastructure, and ecosystems. These impacts depend on the intensity and the magnitude of the hazard and the characteristics of the society hit by the disaster. Investigating and predicting adverse effects of frequent climatic hazards are essential for policy makers and resource managers to plan for the future and be prepared for the consequences of these types of natural disasters. Vulnerability assessments provide a framework to detect the potential threats by exploring the nature of the hazard as well as the political, economic, and social conditions that are expected to affect the capacity of communities to cope with or adapt to that hazard. \nThis research involves the development of a framework for vulnerability assessment of flood and drought at the river basin, sub-catchment, and community scale. The vulnerability assessment method is composed of three major components of exposure, sensitivity, and adaptive capacity. Several indicators are identified to represent these major components of the vulnerability structure. The developed vulnerability assessment has then been implemented on the Upper Ottawa River Basin, Canada. A Geographic Information System-based methodology is used to manage a wide variety of data, to aggregate and integrate several indicators including socio-economic and biophysical indicators, and to visualize the final vulnerability map. The studied areas are categorized in three levels of the vulnerability, high, moderate, and low. North Bay is identified as highly vulnerable to both flood and drought risk. Noranda is also classified as a highly flood-vulnerable area. \nThe vulnerability assessment will provide a valuable insight for mitigation planning as well as prioritizing resource allocation for decision makers. In this research, the location and adequacy of the hydrometric monitoring stations in the Upper Ottawa River Basin are evaluated using the vulnerability map for optimum design of monitoring network.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".