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Record W2054208149 · doi:10.1139/s04-039

A dynamically coupled outfall plume-circulation model for effluent dispersion in Burrard Inlet, British Columbia

2004· article· en· W2054208149 on OpenAlexvenueaboutno aff
Sheng Li, Donald O. Hodgins

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
Fundersnot available
KeywordsOutfallPlumeInletDiffuser (optics)DilutionEnvironmental sciencePanacheAcoustic Doppler current profilerCirculation (fluid dynamics)Dispersion (optics)Hydrology (agriculture)Water qualityEffluentOceanographyCurrent (fluid)GeologyMeteorologyMechanicsEnvironmental engineeringGeographyPhysics

Abstract

fetched live from OpenAlex

A circulation-water quality model, coupled with an outfall plume model, has been developed, to predict three-dimensional circulation and effluent dispersion as functions of circulation forcing, ocean turbulent mixing, discharge rate and diffuser parameters. The coupled model was applied to the Lions Gate outfall discharging sewage into Burrard Inlet, British Columbia; in this case, the circulation was driven by the tides and density variations. The model results have good agreement with dye survey data and acoustic Doppler current profiler (ADCP) measurements from the inlet. Under typical oceanographic conditions for the area, the model predicts submerged plumes below 8 m depth at most times. Short-lived surfacing occurs for a few minutes at slack tide. The near-field trapping level and initial dilution are controlled by tidal flows through First Narrows and the effluent discharge rate. Key words: outfall, diffuser, numerical model, dispersion, circulation, dilution, plume, Burrard Inlet.

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 score0.167
Threshold uncertainty score0.337

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.003
GPT teacher head0.157
Teacher spread0.154 · 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

Citations19
Published2004
Admission routes2
Has abstractyes

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