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Record W4323363152 · doi:10.34133/olar.0006

Why Does Arctic Sea Ice Respond More Evidently than Antarctic Sea Ice to Climate Change?

2023· article· en· W4323363152 on OpenAlexaff
Mohammed Shokr, Yufang Ye

Bibliographic record

VenueOcean-Land-Atmosphere Research · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsSea iceArctic ice packArctic sea ice declineAntarctic sea iceDrift iceCryosphereOceanographyArcticClimate changeClimatologyIcebergGeologyIce-albedo feedbackFast iceArctic geoengineeringGlobal warmingEnvironmental science

Abstract

fetched live from OpenAlex

The current climate change episode has impacted sea ice in the 2 polar regions differently. In the Arctic, remarkable sea ice extent and thickness declines have been observed with a stunning depletion rate of old ice. No similar changes have been observed in the Antarctic. In this paper, the question posed in the title is addressed by reviewing findings retrieved from previous publications. The paper starts by identifying key geographic and climatic features and sea ice characteristics in the 2 polar regions and summarizing relevant recent records. It then proceeds by investigating interactions between sea ice and environmental factors, including atmospheric, oceanic, and dynamic aspects in each region, as well as the increasing number of icebergs in Antarctica. It is concluded that peculiarities of each polar region render the response to climate change differently. Researchers should not apply scenarios regarding the impacts of climate change on Arctic sea ice (i.e., retreat) to Antarctic sea ice. Instead of asking why Antarctic sea ice has not responded to climate change in the same way as Arctic ice, a more reasonable question could be why Arctic ice changes are yielding an annual cycle that resembles that of Antarctic ice. Under current global warming conditions, old ice entrapment within the Arctic basin is relaxed. This could result in Arctic sea ice becoming predominantly seasonal during winter and almost completely melted during summer, which is the current state of Antarctic sea ice.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.003
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 head0.048
GPT teacher head0.318
Teacher spread0.270 · 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 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

Citations24
Published2023
Admission routes1
Has abstractyes

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