MétaCan
Menu
Back to cohort
Record W4254772434 · doi:10.1002/cav.288

Advected river textures

2009· article· en· W4254772434 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

VenueComputer Animation and Virtual Worlds · 2009
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsComputer scienceAdvectionSolverRendering (computer graphics)Computer graphics (images)Fluid dynamicsTexture mappingGeologyMechanics

Abstract

fetched live from OpenAlex

Abstract We present a new method for the realistic real‐time simulation of rivers. Our solution includes a 2D fluid solver that simulates the flow of a river's surface, an efficient method for adaptively computing 3D flow information and an animated 3D procedural wave texture that is advected through the fluid via advection particles in order to mimic the highly detailed fluid surfaces that are characteristic of rivers. Our technique that couples animated texture advection with a pseudo‐3D fluid simulation produces stable results that are representative of large‐scale real‐world rivers and suitable for use in real‐time applications. Our system surpasses prior work on real‐time river rendering both with regards to efficiency and visual quality, which we establish through the rendering of rivers tens of kilometers long. Copyright © 2009 John Wiley & Sons, Ltd.

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)0.000
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score0.600

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 communication0.0000.001
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 head0.012
GPT teacher head0.270
Teacher spread0.258 · 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