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Record W2105361124 · doi:10.1002/2014gl061313

Impact of Weddell Sea deep convection on natural and anthropogenic carbon in a climate model

2014· article· en· W2105361124 on OpenAlexafffund
Raffaele Bernardello, I. Marinov, Jaime B. Palter, Eric D. Galbraith, Jorge L. Sarmiento

Bibliographic record

VenueGeophysical Research Letters · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMcGill University
FundersNational Oceanic and Atmospheric AdministrationSight Research UKNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced ResearchNatural Environment Research CouncilPrinceton University
KeywordsEnvironmental scienceClimate changeConvectionClimatologyDeep seaClimate modelAtmospheric sciencesOceanographyDeep convectionCarbon fibersCarbon cycleGeologyMeteorologyEcosystemEcologyGeographyBiology

Abstract

fetched live from OpenAlex

Abstract A climate model is used to investigate the influence of Weddell Sea open ocean deep convection on anthropogenic and natural carbon uptake for the period 1860–2100. In a three‐member ensemble climate change simulation, convection ceases on average by year 1981, weakening the net oceanic cumulative uptake of atmospheric CO2 by year 2100 (−4.3 Pg C) relative to an ocean that has continued convection. This net weakening results from a decrease in anthropogenic carbon uptake (−10.1 Pg C), partly offset by an increase in natural carbon storage (+5.8 Pg C). Despite representing only 4% of its area, the Weddell Sea is responsible for 22% of the Southern Ocean decrease in total climate‐driven carbon uptake and 52% of the decrease in the anthropogenic component of oceanic uptake. Although this is a model‐specific result, it illustrates the potential of deep convection to produce an intermodel spread in future projections of ocean carbon uptake.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.326
Teacher spread0.300 · 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 designSimulation or modeling
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

Citations53
Published2014
Admission routes2
Has abstractyes

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