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Record W2345234563 · doi:10.1002/2016gl068513

A major increase in winter snowfall during the middle Holocene on western Greenland caused by reduced sea ice in Baffin Bay and the Labrador Sea

2016· article· en· W2345234563 on OpenAlexaboutno aff
Elizabeth K. Thomas, Jason P. Briner, John J. Ryan‐Henry, Yongsong Huang

Bibliographic record

VenueGeophysical Research Letters · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsHoloceneBayOceanographySnowSea iceArcticGeologyArctic ice packClimatologyCryospherePrecipitationGeographyGeomorphology

Abstract

fetched live from OpenAlex

Abstract Precipitation is predicted to increase in the Arctic as temperature increases and sea ice retreats. Yet the mechanisms controlling precipitation in the Arctic are poorly understood and quantified only by the short, sparse instrumental record. We use hydrogen isotope ratios (δ 2 H) of lipid biomarkers in lake sediments from western Greenland to reconstruct precipitation seasonality and summer temperature during the past 8 kyr. Aquatic biomarker δ 2 H was 100‰ more negative from 6 to 4 ka than during the early and late Holocene, which we interpret to reflect increased winter snowfall. The middle Holocene also had high summer air temperature, decreased early winter sea ice in Baffin Bay and the Labrador Sea, and a strong, warm West Greenland Current. These results corroborate model predictions of winter snowfall increases caused by sea ice retreat and furthermore suggest that warm currents advecting more heat into the polar seas may enhance Arctic evaporation and snowfall.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.038
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.260
Teacher spread0.236 · 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 teacher head, 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

Citations81
Published2016
Admission routes1
Has abstractyes

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