Development of a Data Warehouse for Riverine and Coastal Flood Risk Management
Bibliographic record
Abstract
Abstract. In New Brunswick flooding occurs typically during the spring freshet, though, in recent years, midwinter thaws have led to flooding in January or February. Municipalities are therefore facing a pressing need to perform risk assessments in order to identify communities at risk of flooding. In addition to the identification of communities at risk, quantitative measures of potential structural damage and societal losses are necessary for these identified communities. Furthermore, tools which allow for analysis and processing of possible mitigation plans are needed. Natural Resources Canada is in the process of adapting Hazus-MH to respond to the need for risk management. This requires extensive data from a variety of municipal, provincial, and national agencies in order to provide valid estimates. The aim is to establish a data warehouse to store relevant flood prediction data which may be accessed thru Hazus. Additionally, this data warehouse will contain tools for On-Line Analytical Processing (OLAP) and knowledge discovery to quantitatively determine areas at risk and discover unexpected dependencies between datasets. The third application of the data warehouse is to provide data for online visualization capabilities: web-based thematic maps of Hazus results, historical flood visualizations, and mitigation tools; thus making flood hazard information and tools more accessible to emergency responders, planners, and residents. This paper represents the first step of the process: locating and collecting the appropriate datasets.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".