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Record W1670679750 · doi:10.1029/2012gc004263

Evolution of the northeast Labrador Sea during the last interglaciation

2012· article· en· W1670679750 on OpenAlexaboutno aff
Kelsey Winsor, Anders E. Carlson, G. P. Klinkhammer, Joseph S. Stoner, Robert G. Hatfield

Bibliographic record

VenueGeochemistry Geophysics Geosystems · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersUniversity of Wisconsin-MadisonNational Science Foundation
KeywordsGeologyOceanographyHoloceneSea iceHydrographyArcticClimatologySeawater

Abstract

fetched live from OpenAlex

Boreal summer insolation during the last interglaciation (LIG) generally warmed the subpolar to polar Northern Hemisphere more than during the early Holocene, yet regional climate variations between the two periods remain. We investigate northeast Labrador Sea subsurface temperature and hydrography across terminations (T) I and II and during the LIG to assess the impact of two different magnitudes of boreal summer insolation increase on the northeast Labrador Sea. We use Mg/Ca ratios in Neogloboquadrina pachyderma (sinistral) as a proxy of calcification temperature to document changes in subsurface temperatures over Eirik Drift. Our corresponding record of δ 18 O of seawater documents changes in water mass salinity. Mg/Ca calcification temperatures peak early in the Holocene coincident with peak boreal summer insolation. In contrast, LIG temperatures are relatively constant through the interglaciation, and are no warmer than peak Holocene temperatures. During the first half of the LIG, δ 18 O of seawater remains depleted, likely from southern Greenland Ice Sheet retreat and enhanced Arctic freshwater and sea‐ice export to the Labrador Sea. The consequent stratification of the Labrador Sea and attendant suppressed convection explains delayed deep‐ocean ventilation and a cooler subsurface in the northeast Labrador Sea during the LIG.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.004
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.195
Teacher spread0.188 · 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

Citations40
Published2012
Admission routes1
Has abstractyes

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