MétaCan
Menu
Back to cohort
Record W2055749500 · doi:10.1680/wama.11.00067

Particle-tracking model of outfall plumes in a tidal channel

2012· article· en· W2055749500 on OpenAlexaff
Song Liu, S. Samuel Li

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Water Management · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsConcordia University
Fundersnot available
KeywordsEffluentOutfallStratification (seeds)AdvectionPlumeEnvironmental scienceWater columnParticle (ecology)Hydrology (agriculture)Environmental engineeringMeteorologyGeologyOceanographyGeotechnical engineeringGeographyPhysics

Abstract

fetched live from OpenAlex

In order to protect the environment that receives them, it is necessary to manage marine outfall discharges of wastewater effluents, which typically contain different types of contaminants even after treatment. The objective of this paper is to answer the question: ‘To what extent is the receiving environment being exposed to effluents?' The focus is on effluent spreading in the far field. A particle-tracking technique is presented, in which effluents are represented by a large number of particles and the particles' trajectories are tracked for given conditions of ambient flow and density stratification. The technique is particularly useful for examining the undesirable scenario of effluents rising to the water surface or coming into contact with the seabed. Advection, non-Fickian horizontal diffusion and Richardson number-dependent vertical diffusion are taken into account. The technique is successfully applied to the discharge of effluents into a tidal channel where the water column is density-stratified. In this application, predictions of the temporally and spatially varying effluent concentration field agree well with field data. A proper formulation of the effects of stratification on vertical mixing of the effluents in the ambient water is the key to success. The technique has shown advantages in handling large spatial gradients.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.239

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)0.0000.000

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 head0.013
GPT teacher head0.183
Teacher spread0.170 · 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 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

Citations1
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Water ManagementSame topicCoastal and Marine DynamicsFrench-language works237,207