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Record W2904441986 · doi:10.1029/2018gl078608

The Signature of Ozone Depletion in Recent Antarctic Precipitation Change: A Study With the Community Earth System Model

2018· article· en· W2904441986 on OpenAlexaff
Jan T. M. Lenaerts, Jeremy Fyke, Brooke Medley

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsNorth American Construction Group (Canada)
Fundersnot available
KeywordsPrecipitationEnvironmental scienceOzone depletionClimatologyClimate changeAtmospheric sciencesIce coreClimate modelOzoneGeologyOceanographyMeteorologyGeography

Abstract

fetched live from OpenAlex

Abstract Although precipitation is a primary control on Antarctic ice sheet (AIS) mass balance, long‐term historical AIS precipitation trends and their underlying external climate drivers remain inconclusive. In this study, we use a novel pair of climate model ensembles to identify a simulated spatial signature of ozone depletion‐forced AIS precipitation change. Distinct areas of little change or precipitation decrease, arising from interaction between ozone depletion‐forced atmospheric circulation changes and ice sheet topography, are outweighed by large‐scale precipitation increases. This signature bears notable similarities to a new ice core‐based reconstruction of AIS accumulation change and yields a significant increase in annual integrated precipitation (38 ± 10 Gt/year over the 1986–2005 period or 51 ± 11 Gt/year over the 1991–2005 period). Remarkably, this simulated ozone depletion‐forced precipitation change is of a similar absolute magnitude to recent observed AIS mass loss trends and as a consequence, it may play a role in dampening recent AIS sea level rise contributions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.082
GPT teacher head0.306
Teacher spread0.223 · 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

Citations51
Published2018
Admission routes1
Has abstractyes

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