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Record W2337659682 · doi:10.1080/07011784.2015.1126695

Assessing coastal flood risk in a changing climate for the City of Vancouver

2016· article· en· W2337659682 on OpenAlexvenueaboutno aff
Thomas Lyle, Tessa Mills

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsFlood mythCoastal floodTimelineFlooding (psychology)Climate changeEnvironmental resource managementEnvironmental planningFlood risk assessmentAsset (computer security)Vulnerability (computing)Coastal hazardsRisk assessmentGeographySea level riseEnvironmental scienceComputer scienceOceanography

Abstract

fetched live from OpenAlex

Despite global mitigation efforts, climate change will impact the City of Vancouver’s future. One anticipated impact, sea level rise, is described. A comprehensive understanding of the consequences of these impacts is necessary to guide the process of identifying preferred adaptation strategies. A methodology for a coastal flood risk assessment (CFRA) of sea level rise is provided. The inputs for this risk assessment include inundation mapping and an asset-at-risk inventory. These data sets are combined with flood damage information from Hazus to look at consequences of coastal flooding. There are many uncertainties and gaps in the process of developing a CFRA for a modern, dense, urban city such as Vancouver, particularly when the planning timelines required for preparing and adapting to sea level rise are long. The value of the process and results include increased understanding of hazards and vulnerabilities, and the development of useful visual tools for engagement, planning and education.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.014
GPT teacher head0.219
Teacher spread0.205 · 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 designNot applicable
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

Citations10
Published2016
Admission routes2
Has abstractyes

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