Development of a Data Warehouse for Riverine and Coastal Flood Risk Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it