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Record W2022812913 · doi:10.3137/ao.440107

Modelling the Mean Circulation of Baffin Bay

2006· article· en· W2022812913 on OpenAlexaffvenueabout
Ewa Dunlap, Charles Tang

Bibliographic record

VenueATMOSPHERE-OCEAN · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsBayOceanographyGeologyArchipelagoContinental shelfOutflowHydrographyWind stressInflowOcean gyreForcing (mathematics)Current (fluid)Submarine pipelineOcean currentClimatologySubtropics

Abstract

fetched live from OpenAlex

Abstract The Princeton Ocean Model is applied to the Baffin Bay and Labrador region in order to model the summer mean circulation and transport of Baffin Bay. Several aspects of the mean circulation are investigated, but are restricted to the month of September when hydrographic data are available. These include the horizontal and vertical structure of the currents, the sensitivity of the volume transport to the boundary forcing and the effect of the local wind forcing. The model results are shown to be in general agreement with observations. The model reproduces the strongest currents: (a) offshore from the coast of Baffin Island and Ellesmere Island, and (b) at the mouth of Lancaster Sound. The model results also show the presence of the relatively strong southward current along the shelf break on the eastern side of the bay. Strong topographic control is evident in Davis Strait and in the vicinity of deep canyons on the continental shelf of western Greenland. Model sensitivity studies show that the Baffin Bay outflow through western Davis Strait is controlled mainly by the inflow from the archipelago. The inflow through eastern Davis Strait is controlled by both the archipelago inflow and the transport in the Labrador Sea gyre. The local wind stress plays a relatively unimportant role.

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 categoriesInsufficient payload (model declined to judge)
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 score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.186
Teacher spread0.176 · 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.

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

Citations21
Published2006
Admission routes3
Has abstractyes

Explore more

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