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Record W2093259978 · doi:10.1061/40734(145)24

A High-Resolution Coastal Circulation Model for Lunenburg Bay, Nova Scotia

2004· article· en· W2093259978 on OpenAlexaffabout
Jinyu Sheng, Liang Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCoveBarotropic fluidBayGeologyOceanographyForcing (mathematics)Circulation (fluid dynamics)ClimatologyNova scotiaTidal ModelWind stressStorm surgeStormGeomorphology

Abstract

fetched live from OpenAlex

A high-resolution coastal circulation model is used to study the three-dimensional barotropic circulation in Lunenburg Bay and adjacent Upper and Lower South Coves, Nova Scotia. The model is driven by tidal forcing and shelf waves specified at the model open boundaries and wind stress applied at the sea surface. The tidal forcing at the model open boundaries is inferred from the tidal sea level prediction at Lunenburg Harbour. The remotely generated sub-inertial shelf waves that propagate into the model domain through the model open boundaries are calculated by a coarse-resolution storm surge model for the eastern Canadian seaboard. The high-resolution coastal circulation model is used to investigate the nonlinear tidal dynamics in the study region. The model results demonstrate that tidal circulation in the bay and the two coves is highly nonlinear with strong tidal asymmetry between flooding and ebbing, with an intense narrow jet flowing outward from Upper South Cove to Lunenburg Bay during the ebb. The coastal circulation model is also used to simulate the barotropic circulation in Lunenburg Bay during Hurricane Gustav in the second week of September, 2002. The model results demonstrate strong interactions between the local wind stress, tidal forcing, and remotely generated shelf waves during this period.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
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
Bibliometrics0.0000.000
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 head0.036
GPT teacher head0.256
Teacher spread0.220 · 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

Citations2
Published2004
Admission routes2
Has abstractyes

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