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Record W2972599763 · doi:10.1061/9780784482612.066

Understanding Flooding Hazards Posed to Coastal Infrastructure from Extreme Ocean-Driven Events at Future Sea Levels

2019· article· en· W2972599763 on OpenAlexaffabout
D. Chadwick, Ann-Marie Giesbrecht, Nadine Clark, Hammad Mir, Norman Allyn

Bibliographic record

VenuePorts 2019 · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsInro Consultants (Canada)
Fundersnot available
KeywordsCoastal floodFlooding (psychology)Environmental scienceCoastal hazardsOceanographyEnvironmental resource managementComputer scienceEnvironmental planningSea level riseGeologyClimate change

Abstract

fetched live from OpenAlex

The risk of ocean-driven flooding in the vicinity of coastal infrastructure is increasing rapidly as sea levels rise. To facilitate future planning efforts such as port site selection, computer modelling can be performed to determine inundation impacts resulting from sea level rise, storm surge, extreme wind waves, and tsunamis. A vulnerability assessment of infrastructure in 14 communities in British Columbia, Canada, found that some communities will experience significantly shallower tsunami waves or were more sheltered from storm surge and wind wave effects than immediately adjacent shorelines, suggesting that relatively minor changes to a future port or marine terminal’s intended location could have a major impact on the potential level of damage resulting from an extreme ocean-driven event. Additionally, naturally-occurring sea formations such as coral reefs may lend themselves to mitigate the risks associated with extreme events. The methodology and results presented herein can be applied to site selection, infrastructure vulnerability assessments, and other similar evaluations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.027
GPT teacher head0.206
Teacher spread0.180 · 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.

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

Citations1
Published2019
Admission routes2
Has abstractyes

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