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Record W2000013825 · doi:10.1002/vis.304

Simulating the aurora

2003· article· en· W2000013825 on OpenAlexaff
Gladimir V. G. Baranoski, Jon Rokne, Peter Shirley, T. Trondsen, Rui Bastos

Bibliographic record

VenueThe Journal of Visualization and Computer Animation · 2003
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRendering (computer graphics)Natural phenomenonElectronComputer scienceComputer graphicsComputer graphics (images)Excited stateAtmosphere (unit)BrightnessPhysicsNatural (archaeology)OpticsMeteorologyGeologyAtomic physics

Abstract

fetched live from OpenAlex

Abstract We present the first computer graphics algorithm designed to simulate the aurora, a natural phenomenon of great visual beauty and considerable scientific interest. The algorithm is based on the current understanding of the physical origin of this natural display. The aurorae are mainly caused by high‐energy electrons originating in the sun and entering the earth's atmosphere in narrow regions centered on the magnetic poles. These electrons collide with atmospheric atoms, which are excited to higher energy levels. The excited atoms emit rapidly varying visible light in a curtain‐like volume as they return to lower energy levels, thereby creating the aurora. By simulating these light emissions along with the spatial and temporal distribution of the entering electrons, we are able to render the major visual aspects of auroral displays. The applicability of this auroral model for rendering and scientific purposes is illustrated through comparisons of synthetic images with photographs of real auroral displays. Copyright © 2003 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.

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: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.023
GPT teacher head0.309
Teacher spread0.286 · 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

Citations13
Published2003
Admission routes1
Has abstractyes

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