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Record W4224443201 · doi:10.1139/cjce-2021-0539

Relative sea level rise contributions to flood construction levels in British Columbia

2022· article· en· W4224443201 on OpenAlexaffvenueabout
Michael Isaacson

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFlood mythEstimationProbabilistic logicSea levelEnvironmental scienceComputer scienceMeteorologyOperations researchEnvironmental resource managementHydrology (agriculture)Civil engineeringPhysical geographyGeologyStatisticsGeographyEngineeringMathematicsGeotechnical engineering

Abstract

fetched live from OpenAlex

This paper proposes an updated approach to incorporating relative sea level rise in the estimation of flood construction levels in British Columbia, which are required in coastal planning and infrastructure development. Initially, current guidelines for British Columbia are outlined, and recent information on projected relative sea level rise is summarized. Then, recognizing the uncertainty in sea level rise projections, a probabilistic model of estimating the contribution of relative sea level rise to the flood construction level is described, relying on an available theoretical formulation that is corroborated by the application of a Monte Carlo simulation method. An updated approach to incorporating relative sea level rise in the estimation of flood construction levels is thereby recommended, relying on available data portals for relative sea level rise. Finally, the estimation of the probability of overtopping is described, and the implications of this for making informed decisions on flood construction levels with respect to risk assessments are indicated.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.068
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.195
Teacher spread0.188 · 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

Citations0
Published2022
Admission routes3
Has abstractyes

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