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Record W2255497351 · doi:10.1016/j.envsci.2016.01.018

Flood inundation uncertainty: The case of a 0.5% annual probability flood event

2016· article· en· W2255497351 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnvironmental Science & Policy · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilSight Research UKCentre for Environment, Fisheries and Aquaculture ScienceCanadian Centre for Applied Research in Cancer ControlNatural Environment Research CouncilCrown Estate
KeywordsFlood mythEnvironmental scienceEvent (particle physics)Return periodRange (aeronautics)Forcing (mathematics)MarshClimate changeCoastal floodEnvironmental resource managementClimatologyGeographySea level riseEcologyWetlandOceanographyGeologyEngineering

Abstract

fetched live from OpenAlex

Aging coastal defences around the UK are challenging managers to redesign schemes to be resilient to extreme events and climate change, be cost-effective, and have minimal or beneficial environmental impact. To enable effective design, reduced uncertainty in the assessment of flood risk due to natural variability within the coastal forcing is required to focus on conditions that pose highest threat. The typical UK standard of protection for coastal defences is to withstand a 0.5% annual probability event, historically also known as a 1 in 200 year return period event. However, joint wave-water level probability curves provide a range of conditions that meet this criterion. We examine the Dungeness and Romney Marsh coastal zone, a region of high value in terms of habitat and energy assets, to quantify the uncertainty in flood depth and extent generated by a 0.5% probability event, and to explore which combinations of wave and water levels generate the greatest threat.

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.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.006
GPT teacher head0.252
Teacher spread0.246 · 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