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Record W4293213259 · doi:10.1029/2022gl099543

West Antarctic Surface Climate Changes Since the Mid‐20th Century Driven by Anthropogenic Forcing

2022· article· en· W4293213259 on OpenAlexaff
Quentin Dalaiden, Andrew Schurer, Megan C. Kirchmeier‐Young, Hugues Goosse, Gabriele C. Hegerl

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsEnvironment and Climate Change Canada
FundersBelgian Federal Science Policy OfficeFonds De La Recherche Scientifique - FNRSNatural Environment Research CouncilSight Research UK
KeywordsForcing (mathematics)Greenhouse gasEnvironmental scienceClimatologyClimate modelClimate changeOzone depletionOzone layerRadiative forcingAtmospheric sciencesProxy (statistics)SnowOceanographyGeologyMeteorologyGeographyStratosphere

Abstract

fetched live from OpenAlex

Abstract Although the West Antarctic surface climate has experienced large changes over the past decades with widespread surface warming, an overall increase in snow accumulation and a deepening of the Amundsen Sea Low, the exact role of human activities in these changes has not yet been fully investigated, which limits confidence in future projections. Here, we perform a detection and attribution analysis using instrumental and proxy‐based reconstructions, and two large climate model simulation ensembles to quantify the forced response in these observed changes. We show that surface climate changes since the 1950s were driven by anthropogenic forcing, in particular the greenhouse gas forcing and stratospheric ozone depletion. Therefore, our results indicate that the 21st century changes will depend on both the greenhouse gas emissions and the ozone layer recovery.

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)0.001
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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.283
Teacher spread0.246 · 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

Citations28
Published2022
Admission routes1
Has abstractyes

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