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Record W4220742248 · doi:10.1111/jfr3.12800

Storm surge contributions to flood hazards on Canada's Atlantic Coast

2022· article· en· W4220742248 on OpenAlex
Mitchel Provan, Sean Ferguson, Enda Murphy

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Flood Risk Management · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsNational Research Council Canada
FundersNatural Resources CanadaNational Research Council CanadaDefence Research and Development Canada
KeywordsStorm surgeTide gaugeCoastal floodFlood mythStormEnvironmental scienceSurgeFetchClimatologyCurrent (fluid)Sea levelGeologyMeteorologyOceanographyClimate changeGeographySea level rise

Abstract

fetched live from OpenAlex

Abstract A numerical hydrodynamic model was used to simulate the generation and evolution of storm surges in Atlantic Canada in response to synoptic‐scale surface wind and atmospheric pressure fields. The modelling was conducted as part of a broader initiative to support community‐scale inundation modelling and coastal flood risk assessment for communities located in the Acadian Peninsula region of New Brunswick. The 44 largest storm surge events on record at a tide gauge proximate to the region of interest were simulated using the numerical model. Initially, a comparison between simulated storm surges and peak non‐tidal residuals from tide gauge records showed relatively poor agreement, producing an R 2 value of 0.403. Model skill was improved by incorporating the influence of sea ice cover on air‐sea momentum transfer in the hydrodynamic model, and improved correlation with measured residuals was obtained by adding estimates of wave set‐up to the predicted storm surges, ultimately resulting in an R 2 value of 0.803. The results of the simulations provided a basis for identifying distinct regional factors affecting storm surges and water level residuals and demonstrated conditions where wave set‐up and sea ice cover play an important role in contributing to extreme high water levels.

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)0.000
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 score0.381
Threshold uncertainty score0.999

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 integrity0.0000.001
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 head0.007
GPT teacher head0.226
Teacher spread0.219 · 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