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Record W4414798511 · doi:10.1088/2752-5295/ae095b

Focus on Arctic amplification

2025· article· en· W4414798511 on OpenAlexaff
Karen L. Smith, Nicole Feldl, Rodrigo Caballero, Patrick Keys

Bibliographic record

VenueEnvironmental Research Climate · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsArcticClimate changeGlobal warmingArctic sea ice declineThe arcticSea iceArctic geoengineeringArctic ice packEcosystem

Abstract

fetched live from OpenAlex

Abstract Anthropogenic climate change has a disproportionate effect on the Arctic, with the Arctic warming at approximately 2–4 times the rate of the global average, a phenomenon known as Arctic amplification. The greater rate of warming in the Arctic is not only having profound local effects on ecosystems and Indigenous and other communities in the far North, but may also be causing remote effects on weather and climate at lower latitudes. While much has been learned about the climate feedbacks that drive Arctic amplification in response to increasing atmospheric carbon dioxide concentrations, there remain outstanding questions about the evolution of and interactions between climate feedbacks, the relative roles of different climate forcings and feedbacks and local versus remote processes. This focus collection includes 17 articles which contribute novel research findings on (1) the mechanisms driving Arctic amplification with new insights into the time-dependent nature of Arctic amplification and feedback interactions, (2) Arctic amplification across a wide range of CO 2 and non-CO 2 forcings and (3) new approaches to assessing the climate response of amplified Arctic warming and the role of sea ice loss.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.998

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.003

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 head0.025
GPT teacher head0.294
Teacher spread0.268 · 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; both teacher heads agree on what is shown here.

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 routes1
Has abstractyes

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