“The lights are on, but is anyone home?”: Estimating dwelling distribution in rural Alberta
Bibliographic record
Abstract
With Canada's increasing population, natural disasters such as flooding events will have an increasing impact on human populations. The severity of these events requires that decision makers have a clear understanding of the flood risks that communities face in order to plan for and mitigate flood risks. One key component to understanding flood risk is flood exposure, an element of which is the presence of structures (e.g., residences, businesses, and other buildings) in an area that could be damaged by flooding. Presently, several resources exist at both the national and global level that can be used to estimate the spatial distribution of structures. These resources are typically generated at global scales and do not account for regional or local data or processes that could enhance the accuracy and precision of exposure estimation in sparsely populated areas. The present study investigates the feasibility of creating a region-specific dwelling distribution model that helps improve estimation of residential structures in rural areas. Herein, we describe a rural dwelling distribution model for the province of Alberta that can be used to assist in the estimation of structural exposure to flood risk. The model is based on a random forest classification algorithm and several publicly available datasets associated with dwelling and population density. The model was validated using visually referenced data collected from earth imagery. The resulting dwelling layer was then evaluated in its ability to spatially disaggregate census dwelling counts, as well as predict dwelling exposure in several scenarios. This method appears to be a useful alternative to globally scaled models, or using the census alone, particularly for rural areas of Canada.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".