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A simulated distribution of Siberian river runoff in the Arctic Ocean

2019· article· en· W2995810596 on OpenAlexaboutno aff
Elena Golubeva, G. A. Platov, Dina Iakshina, Marina Kraineva

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsHaloclineArcticSurface runoffArctic dipole anomalyOceanographyEnvironmental scienceArctic geoengineeringCanada BasinArctic sea ice declineStratification (seeds)Water massClimatologyArctic ice packSea iceGeologyDrift iceEcologySalinity

Abstract

fetched live from OpenAlex

Abstract Continental runoff is one of the major sources of the Arctic freshwater budget. As is generally known, it influences water column stratification and maintains Arctic halocline, which isolates the sea ice and the cold, fresh upper layer from the warmer, saltier Atlantic waters of the Arctic Ocean. An increase in river runoff was observed in recent years. It is suggested that this will have an impact on Arctic water mass transformations. However, few details are known regarding river freshwater export to the Central Arctic Basin. It is assumed that river water pathways in vast shelf seas and deep basins are closely related to atmospheric variability. In this study, we use three-dimensional coupled regional ocean-ice model simulations forced by atmospheric reanalysis data to investigate the change in Siberian rivers freshwater pathways in the Arctic Ocean due to the variability of atmospheric dynamics. A numerical experiment with an increasing runoff of the largest Siberian rivers is carried out. The consequences of adding freshwater to particular regions of the Arctic Ocean are analysed.

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 score0.011
Threshold uncertainty score0.773

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.173
Teacher spread0.167 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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