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Record W4309026879 · doi:10.3389/fmars.2022.908306

Model simulated freshwater transport along the Labrador current east of the Grand Banks of Newfoundland

2022· article· en· W4309026879 on OpenAlexafffundabout
Yongxing Ma, Youyu Lu, Xianmin Hu, Denis Gilbert, Scott A. Socolofsky, Michel C. Boufadel

Bibliographic record

VenueFrontiers in Marine Science · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsDalhousie UniversityBedford Institute of OceanographyFisheries and Oceans Canada
FundersFisheries and Oceans Canada
KeywordsMesoscale meteorologyIsopycnalFlemishEddyAdvectionCurrent (fluid)Environmental scienceGeologyOceanographyClimatologyGeographyMeteorologyTurbulencePhysicsArchaeology

Abstract

fetched live from OpenAlex

The freshwater transport (FWT) by the Labrador Current (LC) around the Grand Banks of Newfoundland (GBN) is diagnosed with the 26-year Global Ocean Physical Reanalysis 1/12° data (GLORYS12v1) during 1993 - 2018. The time-mean FWT of the LC above the 1027.25 kg/m3 isopycnal surface is 83.6 mSv (1 mSv = 103m3/s) southward through the Flemish Pass. Among this 83.6 mSv, 42% (35.2 mSv) is exported into the interior of the North Atlantic along the whole pathway of the LC from the Flemish Pass to the Tail of the GBN, with 25.5 mSv by the mean advection and 7.2 mSv by the mesoscale eddy transport. The seasonal and inter-annual variations of the FWT in the east of the GBN are mainly caused by the variation of the horizontal velocity of the LC, and the variation of salinity makes a nontrivial contribution to the variation of the FWT to the north of 45°N. Around the Tail of the GBN, the mesoscale eddies make significant contributions to the time-mean FWT and the seasonal and inter-annual variations of the FWT.

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 score0.173
Threshold uncertainty score0.349

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.201
Teacher spread0.191 · 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

Citations4
Published2022
Admission routes3
Has abstractyes

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