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Eddy statistics validation of an ORCA12 ocean and sea ice model for the Arctic with satellite data

2020· article· en· W3097273101 on OpenAlexaboutno aff
Stefanie Rynders, Yevgeny Akesenov, Igor Kozlov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersNatural Environment Research CouncilSight Research UK
KeywordsEddySea iceArcticBoundary currentOceanographyClimatologyGeologySatelliteArctic ice packCanada BasinOcean currentEnvironmental scienceMeteorologyGeographyPhysics

Abstract

fetched live from OpenAlex

As sea ice and ocean models are moving to higher resolution it becomes possible to permit eddy formation even in the Arctic Ocean. Eddies can affect the three dimensional ocean state through causing mixing and even ventilation of subsurface ocean layers if they are deep enough. To ensure models have the potential to simulate the density structure correctly it is therefore necessary to start doing model validation of not only the large scale ocean state, but also of the eddy field. Eddy statistics for the Arctic are available from satellite for the Western Arctic Ocean and the Fram Strait, in particular on number, size and cyclonicity of eddies for open ocean versus ice covered sites. These are compared to a NEMO-LIM 1/12 degree sea ice and ocean simulation (resolution 2-5km), upon which the model based statistics are expanded to the whole Arctic. In the model it is also possible to examine the depth structure of eddies, allowing to generate size vs. depth statistics. This, together with climatological mixed layer depth, provides a first estimate to get satellite-based information on mixing from eddies in the Arctic. We also map the maximum depth of eddies, to examine ventilation and identify sites with especially deep eddies, for instance at the boundary current. Acknowledgements: Grant NE/R000654/1 “Towards a Marginal Sea Ice Cover” funded by the UK Natural Research Council (NERC) and the UK-Russia Arctic bursaries program funded by the United Kingdom’s Department for Business, Energy and Industrial Strategy. The study is also supported from the project “The Advective Pathways of nutrients and key Ecological substances in the Arctic (APEAR)” (grant NE/R012865/1) funded by the Joint UK NERC/German Federal Ministry of Education and Research Changing Arctic Ocean Programme. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 821926 (IMMERSE). IK acknowledges the support from RFBR grant No 18-35-20078.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.128
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.044
GPT teacher head0.241
Teacher spread0.197 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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