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Record W2055668145 · doi:10.1680/cien.2011.164.3.129

Keeping nuclear and other coastal sites safe from climate change

2011· article· en· W2055668145 on OpenAlexaff
Robert L. Wilby, Robert J. Nicholls, Rachel Warren, H. S. Wheater, D. Clarke, Richard Dawson

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Civil Engineering · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsUniversity of Saskatchewan
FundersEngineering and Physical Sciences Research CouncilNatural Environment Research CouncilSight Research UK
KeywordsNuclear powerClimate changeFlexibility (engineering)Nuclear power plantEnvironmental scienceNuclear plantAdaptation (eye)Plan (archaeology)Nuclear disasterEnvironmental resource managementClimatologyEnvironmental planningGeographyOceanographyEngineeringGeologyArchaeologyEcologyNuclear engineering

Abstract

fetched live from OpenAlex

The UK’s eight proposed new nuclear power stations are all to be sited on the coast. With a total cradle-to-grave life cycle of at least 160 years, and heightened awareness of inundation risk following the failure of the Fukushima I nuclear plant in Japan this year, Britain’s nuclear developers have to show how they plan to cope with the possibility of rising sea levels, higher sea temperatures and more extreme weather events over the next two centuries. This paper describes the adaptation options for new nuclear and other major long-lived coastal developments. Despite uncertainty about climate scenarios for the 2200s, it explains how flexibility of design and safety margins can be incorporated from the outset and, when combined with routine environmental monitoring, how sites can be adaptively managed throughout their life cycles.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.645

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)0.0000.000

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 head0.016
GPT teacher head0.187
Teacher spread0.170 · 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

Citations34
Published2011
Admission routes1
Has abstractyes

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