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Record W2004685882 · doi:10.1175/jpo2708.1

On Deep-Water Renewals in Indian Arm, British Columbia: Sensitivity to the Production of Turbulent Kinetic Energy Caused by Horizontal Variations in the Flow Field

2005· article· en· W2004685882 on OpenAlexafffundabout
Michael W. Stacey, S. Pond

Bibliographic record

VenueJournal of Physical Oceanography · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of British ColumbiaRoyal Military College of Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSillTurbulenceInletTurbulence kinetic energyMixing (physics)MechanicsCirculation (fluid dynamics)Kinetic energyGeologyFlow (mathematics)Sensitivity (control systems)MeteorologyEnvironmental sciencePhysicsOceanographyClassical mechanicsPetrologyEngineering

Abstract

fetched live from OpenAlex

Abstract A two-dimensional (i.e., laterally averaged) numerical model of the circulation in Burrard Inlet and Indian Arm near British Columbia, Canada, is used to examine the sensitivity of deep-water renewal events in Indian Arm to the turbulent mixing in the lee of the narrow sills in Burrard Inlet. Horizontal variations in the flow field can have an important influence on the production of turbulent kinetic energy near the sills and therefore also on the renewal events in Indian Arm. An ad hoc modification to the expression for the production of turbulent kinetic energy, required to obtain an acceptable simulation downstream of Second Narrows in Burrard Inlet, also results in a reasonable simulation of the observed circulation in Indian Arm. The modified laterally averaged model can reproduce the main features of the circulation away from the narrow sills. However, it seems that a three-dimensional model will be required if the circulation is to be simulated with greater accuracy and without the ad hoc modification, which has a free parameter.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.931

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)0.0000.000

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 head0.004
GPT teacher head0.181
Teacher spread0.178 · 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
Published2005
Admission routes3
Has abstractyes

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