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Record W2022129734 · doi:10.3390/su4092176

A Climate Change Adaptation Planning Process for Low-Lying, Communities Vulnerable to Sea Level Rise

2012· article· en· W2022129734 on OpenAlexaffabout
Sara Barron, Glenis Canete, Jeff Carmichael, David Flanders, Ellen Pond, Stephen R.J. Sheppard, Kristi Tatebe

Bibliographic record

VenueSustainability · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsPembina InstituteUniversity of British Columbia
Fundersnot available
KeywordsFlood mythEnvironmental planningAdaptation (eye)Environmental resource managementVulnerability (computing)Context (archaeology)Coastal floodClimate changeBusinessGeographyComputer scienceEnvironmental scienceSea level rise

Abstract

fetched live from OpenAlex

While the province of British Columbia (BC), Canada, provides guidelines for flood risk management, it is local governments’ responsibility to delineate their own flood vulnerability, assess their risk, and integrate these with planning policies to implement adaptive action. However, barriers such as the lack of locally specific data and public perceptions about adaptation options mean that local governments must address the need for adaptation planning within a context of scientific uncertainty, while building public support for difficult choices on flood-related climate policy and action. This research demonstrates a process to model, visualize and evaluate potential flood impacts and adaptation options for the community of Delta, in Metro Vancouver, across economic, social and environmental perspectives. Visualizations in 2D and 3D, based on hydrological modeling of breach events for existing dike infrastructure, future sea level rise and storm surges, are generated collaboratively, together with future adaptation scenarios assessed against quantitative and qualitative indicators. This ‘visioning package’ is being used with staff and a citizens’ Working Group to assess the performance, policy implications and social acceptability of the adaptation strategies. Recommendations based on the experience of the initiative are provided that can facilitate sustainable future adaptation actions and decision-making in Delta and other jurisdictions.

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 score0.098
Threshold uncertainty score0.535

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.088
GPT teacher head0.354
Teacher spread0.266 · 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

Citations42
Published2012
Admission routes2
Has abstractyes

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