Mapping the Past and Informing the Future: A Framework for Accessing and Communicating Historic Canadian Flood Impact Data
Bibliographic record
Abstract
ABSTRACT Flooding is one of Canada's most frequent and costly natural disasters, with major negative impacts on people, economies, and ecosystems. Yet, historic Canadian flood impact data remain fragmented, inconsistent, and difficult for both the public and policymakers to access. This research develops a framework for collecting, standardizing, and visualizing historic flood impact data to improve natural disaster communication and public safety. Using the Canadian Disaster Database as a foundation, flood events from 2010 to 2024 were analyzed and assigned standardized impact scores reflecting social, environmental, and economic costs. These data were stored in a dynamic PostgreSQL database that automates calculations and supports future updates. An interactive Geographic Information System (GIS) platform was created to visualize this information, allowing users to explore flood histories across Canada, view event details, and track changing regional vulnerability through time. Unlike traditional flood maps that emphasize water extent or depth, this tool focuses on accessibility and interpretation, bridging scientific information with public awareness and policy development. By making complex flood data transparent and interactive, this framework advances science communication, strengthens evidence‐based decision‐making, and supports a more resilient Canada in the face of increasing climate‐related risks.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".