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Record W2012402942 · doi:10.1126/science.1254961

Greenland temperature response to climate forcing during the last deglaciation

2014· article· en· W2012402942 on OpenAlexaff
Christo Buizert, Vasileios Gkinis, Jeffrey P. Severinghaus, Feng He, Benoit S. Lecavalier, P. Kindler, Markus Leuenberger, Anders E. Carlson, Bo Vinther, Valérie Masson‐Delmotte, James W. C. White, Zhengyu Liu, Bette L. Otto‐Bliesner, Edward J. Brook

Bibliographic record

VenueScience · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDeglaciationForcing (mathematics)ClimatologySeasonalityPhysical geographyPerspective (graphical)Climate changeSurface air temperatureGeologyEnvironmental scienceOceanographyAtmospheric sciencesGeographyHoloceneEcologyBiologyMathematics

Abstract

fetched live from OpenAlex

Greenland ice core water isotopic composition (δ(18)O) provides detailed evidence for abrupt climate changes but is by itself insufficient for quantitative reconstruction of past temperatures and their spatial patterns. We investigate Greenland temperature evolution during the last deglaciation using independent reconstructions from three ice cores and simulations with a coupled ocean-atmosphere climate model. Contrary to the traditional δ(18)O interpretation, the Younger Dryas period was 4.5° ± 2°C warmer than the Oldest Dryas, due to increased carbon dioxide forcing and summer insolation. The magnitude of abrupt temperature changes is larger in central Greenland (9° to 14°C) than in the northwest (5° to 9°C), fingerprinting a North Atlantic origin. Simulated changes in temperature seasonality closely track changes in the Atlantic overturning strength and support the hypothesis that abrupt climate change is mostly a winter phenomenon.

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)0.001
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 score0.056
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.000

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 head0.008
GPT teacher head0.236
Teacher spread0.228 · 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

Citations371
Published2014
Admission routes1
Has abstractyes

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