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Record W4297513501 · doi:10.1080/07011784.2022.2122083

Flood risk assessment data access and equity in Metro Vancouver

2022· article· en· W4297513501 on OpenAlexafffundvenueabout
Chris Gouett-Hanna, Greg Oulahen, Daniel Henstra, Jason Thistlethwaite

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsUniversity of WaterlooToronto Metropolitan University
FundersMarine Environmental Observation Prediction and Response Network
KeywordsEquity (law)Flood mythBusinessEnvironmental planningGeographyPolitical scienceArchaeology

Abstract

fetched live from OpenAlex

Flood risk is increasing in many urban regions in Canada. Flood risk is the product of interaction between a flood hazard, the exposure of built assets, and the vulnerability of people to flood impacts. Flood risk assessment seeks to quantify each of these factors for a geographic space to identify areas with greater risk, which can inform public- and private-sector decision making. This study conducts a flood risk assessment of Metro Vancouver in British Columbia using only government-provided open data. It finds that flood hazards and social vulnerability are uneven across the study area, and it reveals inequity in open data access and quality between municipalities. It concludes that more standardized or centralized provision of open data could better support flood risk analysis in Metro Vancouver, which could inform flood management and help to reduce local risk.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0020.001
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.282
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2022
Admission routes4
Has abstractyes

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