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Record W4400776970 · doi:10.4236/gep.2024.126020

Development and Applications of a Coupled Circulation-Ice Modelling System with Nesting Capacity for the Northwest Atlantic

2024· article· en· W4400776970 on OpenAlexafffundabout
Jinyu Sheng, Qiantong Pei, Yi Sui, Bo Yang, Shengmu Yang, Kyoko Ohashi

Bibliographic record

VenueJournal of Geoscience and Environment Protection · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsDalhousie University
FundersCanadian Space AgencyFisheries and Oceans CanadaOcean Frontier InstituteNatural Sciences and Engineering Research Council of CanadaMarine Environmental Observation Prediction and Response Network
KeywordsNesting (process)Circulation (fluid dynamics)Environmental scienceClimatologyOceanographyFisheryGeologyEngineeringBiologyAerospace engineering

Abstract

fetched live from OpenAlex

A coupled circulation-ice modelling system with multi-grid nesting capacity was developed for the northwest Atlantic (CCIMS-nwA) based on the Regional Ocean Modeling System (ROMS) and Los Alamos Sea Ice Model (CICE). CCIMS-nwA is forced by reanalysis-derived atmospheric fields at the surface, tidal forcing, inflows and ice conditions specified at lateral open boundaries, riverine freshwater discharges, and continental runoff due to melting of ice and snow over land. Different nested-grid setups were used based on requirements of different projects. Performance of CCIMS-nwA is assessed using the in-situ oceanographic observations, satellite remote sensing data, and ocean reanalysis. Model results in three applications demonstrate the feasibility and skills of CCIMs-nwA in simulating both the large-scale hydrodynamics over the eastern Canadian shelf and fine-resolution currents and hydrography over coastal waters.

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)0.002
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 score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.019
GPT teacher head0.179
Teacher spread0.160 · 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
Published2024
Admission routes3
Has abstractyes

Explore more

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