MétaCan
Menu
Back to cohort
Record W3021912068 · doi:10.1029/2020gl088100

Sea Ice Retreat Contributes to Projected Increases in Extreme Arctic Ocean Surface Waves

2020· article· en· W3021912068 on OpenAlexaff
Mercè Casas‐Prat, Xiaolan L. Wang

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsCoupled model intercomparison projectArcticClimatologySea iceThe arcticFetchEnvironmental scienceWind waveArctic sea ice declineClimate modelArctic ice packClimate changeGeologyAtmospheric sciencesOceanographyDrift ice

Abstract

fetched live from OpenAlex

Abstract The projected changes and trends in the regional annual and monthly maxima of the significant wave height (Hs) in the Arctic Ocean are studied using wave simulations derived from the CMIP5 (Coupled Model Intercomparison Project Phase 5) climate simulations for 1979–2005 and 2081–2100 periods. Under the RCP8.5 scenario, the regional annual maximal Hs increases on average up to ∼3 cm/year, or >0.5%/year, relative to the 1986–2005 climatological value, in many Arctic areas (and up to 0.8%/year in the east side of the Arctic Ocean). While strong winds need to occur for large waves to develop, the changes in wind speed alone cannot explain the increases in the regional maximal Hs. Sea ice retreat also plays an important role by increasing fetch to promote wave growth, thereby contributing notably to the projected increase in wave height. It also contributes to increasing the probability of strong winds over the widening ice‐free waters.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.056
GPT teacher head0.270
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations56
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueGeophysical Research LettersSame topicArctic and Antarctic ice dynamicsFrench-language works237,207