Regional Analysis of Population Exposure to Flooding in Canada
Bibliographic record
Abstract
Over the years, flooding is becoming more of a significant cause of casualties, economic loss, property & environmental damages all over the world due to climate change, severe precipitation, land use changes and numerous other factors. Studies have proven that flooding is predicted to occur even more frequently in the near future. With the rise of flood-prone areas in Canada specifically, it has now become more crucial than ever to consider floodplain mapping at larger scales which can help us identify different degrees of risk and vulnerability for unique locations. This process has become much easier with the availability of accurate global flood models such as CaMa-Flood, and the public release of a huge variety of datasets (including population datasets and reanalysis data). In this report, we will be analyzing how much of the Canadian population is exposed to floods and the risk factors. In doing so, we also present a pre-processed flood map that was formulated using the CaMa-Flood model and freely available NARR reanalysis data used as the input values. We also introduce different global population data sources and analyse differences between them, which are needed for the further assessment of impacts of flooding. Lastly, this study will touch upon what high and very high flood depths are based on the hazards they pose to humans, and how it is relevant to exposure analysis. This report demonstrates a detailed presentation of the process (split up into three experiments) needed to be followed on ArcGIS to properly assess the exposure of the Canadian population to flooding, where any user familiar with the content who has the software can follow along without any issues.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".