MétaCan
Menu
Back to cohort
Record W4210680328 · doi:10.3390/geosciences12020056

Geophysical and Societal Dimensions of Floods in Manitoba, Canada: A Social Vulnerability Assessment of the Rural Municipality of St. Andrews

2022· article· en· W4210680328 on OpenAlexaffabout
C. Emdad Haque, Khandakar Hasan Mahmud, David J. Walker, Jobaed Ragib Zaman

Bibliographic record

VenueGeosciences · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFlood mythSocial vulnerabilityDamagesGeographyVulnerability (computing)Government (linguistics)Socioeconomic statusDelphi methodEnvironmental planningEnvironmental resource managementEnvironmental sciencePopulationPolitical scienceSociologyArchaeologyPsychological resilience

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.264
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueGeosciencesSame topicFlood Risk Assessment and ManagementFrench-language works237,207