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Record W7073792629

Subsurface ocean warming preceded Heinrich Events

2022· article· en· W7073792629 on OpenAlexaboutno aff

Bibliographic record

VenueKölner Universitäts PublikationsServer (Universität zu Köln) · 2022
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsThermohaline circulationGlacial periodNorth Atlantic Deep WaterAbrupt climate changeGlacierGlobal warmingOcean currentClimate changeArctic
DOInot available

Abstract

fetched live from OpenAlex

Although the global environmental impact of Laurentide Ice-Sheet destabilizations on glacial climate during Heinrich Events is well-documented, the mechanism driving these ice-sheet instabilities remains elusive. Here we report foraminifera-based subsurface (similar to 150 m water depth) ocean temperature and salinity reconstructions from a sediment core collected in the western subpolar North Atlantic, showing a consistent pattern of rapid subsurface ocean warming preceding the transition into each Heinrich Event identified in the same core of the last 27,000 years. These results provide the first solid evidence for the massive accumulation of ocean heat near the critical depth to trigger melting of marine-terminating portions of the Laurentide Ice Sheet around Labrador Sea followed by Heinrich Events. The repeated build-up of a subsurface heat reservoir in the subpolar Atlantic closely corresponds to times of weakened Atlantic Meridional Overturning Circulation, indicating a precursor role of ocean circulation changes for initiating abrupt ice-sheet instabilities during Heinrich Events. We infer that a weaker ocean circulation in future may result in accelerated interior-ocean warming of the subpolar Atlantic, which could be critical for the stability of modern, marine-terminating Arctic glaciers and the freshwater budget of the North Atlantic.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.003
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.224
Teacher spread0.208 · 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.

Study designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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