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Record W4413417103 · doi:10.3390/jmse13081562

Arctic Wave Climate Including Marginal Ice Zone and Future Climate Scenario

2025· article· en· W4413417103 on OpenAlexafffund
Hamid Goharnejad, William Perrie, Bechara Toulany, Minghong Zhang, Zhenxia Long, M. Casey, Michael H. Meylan

Bibliographic record

VenueJournal of Marine Science and Engineering · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsDalhousie UniversityBedford Institute of OceanographyFisheries and Oceans Canada
FundersFisheries and Oceans Canada
KeywordsEnvironmental scienceArcticClimatologyClimate changeThe arcticArctic ice packSea iceAtmospheric sciencesOceanographyGeology

Abstract

fetched live from OpenAlex

This study examines the variation and trends in wave parameters across the Arctic, including the marginal ice zone (MIZ), by comparing historical data (1980–2009) with projections for a future climate scenario (2070–2099) as outlined by the IPCC. Utilizing the WAVEWATCH III (WW3) numerical wave prediction model, we simulate the wave climate for these periods, incorporating advanced parameterizations to account for wave-ice interactions within the MIZ. Our analysis focuses on the extreme values of significant wave heights (Hs), mean wave periods (T0), and dominant mean wave direction (MWD), calculated for both winter and summer seasons. To assess changes in wave climate under future climate scenarios, we first use a similarity matrix, applying the kappa variable and cell-by-cell numerical comparison methods to assess model congruence across different conditions. We also follow a standard approach, by assessing the extreme wave conditions for 20 and 100-year return periods using standard stochastic models, including Gumbel, exponential, and Weibull distributions.

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)0.001
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 score0.294
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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
Published2025
Admission routes2
Has abstractyes

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