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Record W4387857004 · doi:10.1098/rsta.2022.0191

Overturning in the subpolar North Atlantic: a review

2023· review· en· W4387857004 on OpenAlexaboutno aff
M. Susan Lozier

Bibliographic record

VenuePhilosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2023
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
FundersNatural Environment Research CouncilMet OfficeNational Science Foundation
KeywordsOceanographyGulf StreamGeology

Abstract

fetched live from OpenAlex

The Overturning in the Subpolar North Atlantic Program (OSNAP) was initiated in the spring of 2010 through a collaborative effort involving the USA, the UK, Germany, the Netherlands and Canada. A key feature of OSNAP is a trans-basin observing system deployed in the summer of 2014 for the continuous measure of volume, heat and freshwater fluxes in the subpolar North Atlantic. This review focuses on advancements made possible by the collective OSNAP observations. Chief among those advancements is the quantification of the dominant role of the eastern subpolar North Atlantic in the production of dense waters that reside in the lower limb of the overturning: the Irminger and Iceland basins contributed approximately three times as much dense water compared with the Labrador Sea over the observational period. Other advancements include elucidation of the relationship between convective activity in the basin interior and boundary current anomalies; the spread of overflow waters in the subpolar region; the seasonality of the meridional volume, heat and freshwater fluxes; and the challenges involved in designing a simpler, less costly observing system. Collectively, OSNAP measurements are laying a framework on which to assess the overturning circulation's vulnerability to continued warming and freshening as climate change continues apace. This article is part of a discussion meeting issue 'Atlantic overturning: new observations and challenges'.

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)0.001
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.558
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.259
Teacher spread0.221 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations27
Published2023
Admission routes1
Has abstractyes

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