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Record W2067145150 · doi:10.1029/2006gl026124

Sensitivity of the thermohaline circulation to Arctic Ocean runoff

2006· article· en· W2067145150 on OpenAlexafffund
Å. K. Rennermalm, Eric F. Wood, Stephen J. Déry, Andrew J. Weaver, Michael Eby

Bibliographic record

VenueGeophysical Research Letters · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsOcean Networks Canada SocietyUniversity of VictoriaUniversity of Northern British Columbia
FundersCanada Research ChairsGovernment of Canada
KeywordsThermohaline circulationSurface runoffEnvironmental scienceArcticOceanographyArctic dipole anomalyOcean currentArctic geoengineeringClimatologyGeologyArctic ice packEcologyDrift ice

Abstract

fetched live from OpenAlex

Arctic Ocean river runoff increases over the 20th century raise concerns of the potential impact it may have on the thermohaline circulation (THC) and thus global climate. This study investigates how changes in Arctic river discharge may control THC by a series of experiments with an intermediate complexity global climate model. The experiments show an inverse relationship between THC strength and changes to riverine freshwater discharge, similar to the response of THC to surface freshening of the North Atlantic. Arctic Ocean freshwater export and volume were more sensitive to river runoff than sea ice export. A strong linear relationship between the THC strength and the steric height gradient (depth integrated density anomaly and an important driver for the western boundary current) suggests that the Arctic freshwater pools and fluxes are very effective in translating changes in runoff to THC strength by regulating the ocean water density in 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 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)0.000
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.018
GPT teacher head0.253
Teacher spread0.235 · 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

Citations32
Published2006
Admission routes2
Has abstractyes

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