Geophysical and Societal Dimensions of Floods in Manitoba, Canada: A Social Vulnerability Assessment of the Rural Municipality of St. Andrews
Bibliographic record
Abstract
Being strongly influenced by the landscape of the Red River Valley, geophysical and a variety of sociodemographic and economic factors, the characteristics of floods are complex in the Province of Manitoba, Canada, which causes substantial loss and damage to lives and properties. The primary objectives of this study are two-fold: (i) to identify the geophysical and human-induced conditions of floods, and examine the trend in flood loss and damage in the Province of Manitoba, Canada; and (ii) to analyze the social vulnerability perspectives of floods in the Rural Municipality of St. Andrews, as a local community case study. Using the Delphi technique, primary data were procured from the field for community-level vulnerability analysis. Secondary data for a provincial-level analysis were collected from various public domains, including governmental departments and other non-government sources. The results reveal that a nested set of geophysical and societal factors determine the degree of vulnerability of individual community members. In Manitoba, it was found that socioeconomic damages caused by floods have increased considerably over time despite undertaking costly structural flood mitigation measures. We conclude that minimization of flood damages requires complementing structural measures with knowledge-sharing, collaboration among pertinent institutions, and the adoption of an interactive flood management system approach.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".