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Record W2047972613 · doi:10.1029/2003gl017433

The impact of varying atmospheric forcing on the thickness of arctic multi‐year sea ice

2003· article· en· W2047972613 on OpenAlexaff
Jacqueline Dumas, Gregory M. Flato, Andrew J. Weaver

Bibliographic record

VenueGeophysical Research Letters · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSea iceArctic ice packArctic sea ice declineClimatologyArctic oscillationArcticForcing (mathematics)GeologyArctic geoengineeringArctic dipole anomalyDrift iceAnomaly (physics)Sea ice thicknessAtmospheric sciencesOceanographyPhysicsNorthern Hemisphere

Abstract

fetched live from OpenAlex

A 1‐D thermodynamic sea ice model, forced with North Pole Drift Station observations from 1954–91, is used to study the effect of changing atmospheric forcing on multi‐year Arctic sea ice. From 1954–70, most seasons show positive trends in calculated sea ice thickness over much of the Arctic. A dip in calculated ice thickness takes place between 1971–77 over most of the Arctic. Following the North Pacific regime shift in 1976–1977, the period 1978–91 reveals large negative trends in calculated sea ice thickness in all seasons. The results indicate that an important part of the variability and trends in Arctic sea ice thickness is thermodynamically‐driven. Of the total variance in multi‐year sea ice thickness, 10 to 20% is explained by variations in the Arctic Oscillation and Pacific North American patterns. The multi‐year ice thickness response to a positive wintertime Arctic Oscillation anomaly occurs the following summer and persists for more than a year.

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)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.293
Teacher spread0.261 · 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

Citations13
Published2003
Admission routes1
Has abstractyes

Explore more

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