MétaCan
Menu
Back to cohort
Record W2179136768 · doi:10.1038/ncomms9939

Fennoscandian freshwater control on Greenland hydroclimate shifts at the onset of the Younger Dryas

2015· article· en· W2179136768 on OpenAlexfundno aff
Francesco Muschitiello, Francesco S. R. Pausata, Jenny E. Watson, Rienk H. Smittenberg, Abubakr A. M. Salih, Stephen J. Brooks, Artemis Karlatou-Charalampopoulou, Barbara Wohlfarth

Bibliographic record

VenueNature Communications · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersQueen's UniversityQueen's University BelfastDepartment for Employment and Learning, Northern Ireland
KeywordsYounger DryasAbrupt climate changeGeologyOceanographyStadialIce coreClimatologyClimate changeGreenland ice sheetIce sheetHoloceneGlobal warmingEffects of global warming

Abstract

fetched live from OpenAlex

Sources and timing of freshwater forcing relative to hydroclimate shifts recorded in Greenland ice cores at the onset of Younger Dryas, ∼12,800 years ago, remain speculative. Here we show that progressive Fennoscandian Ice Sheet (FIS) melting 13,100-12,880 years ago generates a hydroclimate dipole with drier-colder conditions in Northern Europe and wetter-warmer conditions in Greenland. FIS melting culminates 12,880 years ago synchronously with the start of Greenland Stadial 1 and a large-scale hydroclimate transition lasting ∼180 years. Transient climate model simulations forced with FIS freshwater reproduce the initial hydroclimate dipole through sea-ice feedbacks in the Nordic Seas. The transition is attributed to the export of excess sea ice to the subpolar North Atlantic and a subsequent southward shift of the westerly winds. We suggest that North Atlantic hydroclimate sensitivity to FIS freshwater can explain the pace and sign of shifts recorded in Greenland at the climate transition into the Younger Dryas.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.079
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.276
Teacher spread0.247 · 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

Citations75
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueNature CommunicationsSame topicGeology and Paleoclimatology ResearchFrench-language works237,207