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Record W2424369771 · doi:10.1109/oceansap.2016.7485528

Modeling the dispersion of tracers in the marine environment: A model sensitivity study

2016· article· en· W2424369771 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOCEANS 2016 - Shanghai · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSensitivity (control systems)Dependency (UML)Eulerian pathTRACERDispersion (optics)Computer scienceTracking (education)GridParticle (ecology)Computer simulationParticle numberDiffusionBiological systemMathematicsSimulationVolume (thermodynamics)Applied mathematicsPhysicsLagrangianEngineeringElectronic engineeringOpticsGeologyArtificial intelligence

Abstract

fetched live from OpenAlex

Precise and efficient numerical simulation of tracer transport processes in the marine environment is critical for environmental problems. Particle tracking method has the advantages over Eulerian method by overcome the problem of numerical diffusion but the concentration from typical particle tracking methods is affected by the number of particles used and the size of the volume cell. DREAM model used a hybrid numerical analytical approach to reduce its dependency on these parameters. A sensitivity study has been conducted to evaluate DREAM model's sensitivity to a number of parameters including: number of particles, resolution of grid, time steps, and grid systems. The results indicated that DREAM is a robust model and it has little dependency on these parameters, except at early stages of the release.

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.

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)0.001
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 score0.342
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.214
Teacher spread0.201 · 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