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IMPACT OF CLIMATE CHANGE-INDUCED SEA ICE RETREAT ON ARCTIC STORM SURGES

2023· article· en· W4386960505 on OpenAlexaffabout
Joseph Kim, Ioan Nistor, Enda Murphy, Sean Ferguson, Mitchel Provan

Bibliographic record

VenueCoastal Engineering Proceedings · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
Fundersnot available
KeywordsStorm surgeClimate changeSea iceArctic ice packCoastal erosionArctic sea ice declineArcticStormOceanographyClimatologyEnvironmental sciencePermafrostArctic geoengineeringSurgeGeologyAntarctic sea iceShoreGeomorphology

Abstract

fetched live from OpenAlex

Canada’s Arctic coastline is significantly affected by climate change factors such as relative sea-level rise, thawing permafrost, sea ice retreat, accelerating coastal erosion and increasingly extreme wave climate (Environment Canada, 2019). These factors exacerbate the impacts of storm surges and associated coastal flooding of Arctic communities. While there have been studies studying the impact of sea ice retreat on wave climate (Casas‐Prat and Wang, 2020; Waseda et al., 2021), there have not been any studies on its effect on storm surge development. This is the first study quantifying the projected increase in storm surge hazard along the coast of the Beaufort Sea due to climate change-induced sea ice retreat and shortening of ice season duration through numerical modelling.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.229
Teacher spread0.209 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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