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Record W2318583651 · doi:10.1061/9780784412411.00006

Application of Finite-Volume Coastal Ocean Model in Studying Strong Tidal Currents in Discovery Passage, British Columbia, Canada

2012· article· en· W2318583651 on OpenAlexaffabout
Yuehua Lin, Jianhua Jiang, David B. Fissel, Michael Foreman, P.G. Willis

Bibliographic record

VenueEstuarine and Coastal Modeling · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsFisheries and Oceans CanadaASL Environmental Sciences (Canada)
Fundersnot available
KeywordsStratification (seeds)Unstructured gridFinite volume methodDischargeOceanographyCurrent (fluid)GeologyEstuaryTidal currentHydrological modellingOcean currentHydrology (agriculture)Environmental scienceClimatologyGridGeographyPhysics

Abstract

fetched live from OpenAlex

The unstructured-grid, Finite-Volume Coastal Ocean Model (FVCOM) was used to simulate the flows in Discovery Passage, British Columbia, Canada. Challenges in this numerical study include the strong tidal currents in Seymour Narrows of up to 7.8 m s-1, small-scale topographic features, and freshwater discharge and stratification. Tidal forcing, freshwater input, the Coriolis effect, and wet and dry regions were considered. The model was integrated for 16 days and model results of the last 14 days were examined. The model was validated using available historical measurements at different sites in Discovery Passage, including water surface elevation and ocean current data, as well as CTD-bottle profile data. Model results are also compared with the recent numerical studies by Jiang and Fissel (2007) and by Foreman et al. (2012). Model results demonstrated that the unstructured-grid model generated reasonable maps of the very strong currents in tidal channels, with the advantage of high adaptability in resolving the complex geometry of the narrow channels as seen in Discovery Passage. Effects of stratification and freshwater discharge from Campbell River during the study period were investigated.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.612

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.196
Teacher spread0.184 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2012
Admission routes2
Has abstractyes

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