MétaCan
Menu
← Back to cohort
Record W4234163203 · doi:10.5194/gmd-2020-111

Introducing LAB60: A 1/60° NEMO 3.6 numerical simulation of the Labrador Sea

2020· preprint· en· W4234163203 on OpenAlexafffundabout
Clark Pennelly, Paul G. Myers

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOceanographyGeologyEddyStratification (seeds)Horizontal resolutionOcean currentSea iceConvectionClimatologyMeteorologyGeographyTurbulence

Abstract

fetched live from OpenAlex

Abstract. A high-resolution coupled ocean-sea ice model is set up within the Labrador Sea. With a horizontal resolution of 1/60°, this simulation is capable of resolving the multitude of eddies which transport heat and freshwater into the interior of the Labrador Sea. The transport of these fluxes strongly governs the overall stratification, deep convection, and subsequent production of Labrador Sea Water. We implement nested domains within our regional configuration to reduce computational costs, allowing for a simulation that spans over 10 years. Three passive tracers are also included: Greenland runoff, Labrador Sea Water produced during convection, and Irminger Water which enters the Labrador Sea along Greenland. We describe the configuration setup and compare against similarly forced lower-resolution simulations to better describe how horizontal resolution impacts the Labrador Sea.

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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.015
GPT teacher head0.224
Teacher spread0.209 · 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

Citations3
Published2020
Admission routes3
Has abstractyes

Explore more

Same topicOceanographic and Atmospheric Processes→French-language works237,207→