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Record W2065730018 · doi:10.3141/1899-22

Visualizing Massive Terrain with Transportation Infrastructure by Using Continuous Level of Detail

2004· article· en· W2065730018 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

VenueTransportation Research Record Journal of the Transportation Research Board · 2004
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTerrainRendering (computer graphics)Terrain renderingComputer scienceQuadtreeProcess (computing)Remote sensingComputer graphics (images)GeologyComputer visionGeographyCartography

Abstract

fetched live from OpenAlex

An approach to the efficient rendering of terrain surfaces through the use of continuous level of detail but with maintenance of the integrity of added transportation features such as roads, bridges, railways, bus stops, and traffic lights is explored. A quadtree structure was developed to define the terrain surface as a gridded height field and, in the rendering process, to project transportation features in three dimensions onto the terrain surface on the fly. Also, with the viewpoint moving, the connected strips and fans between transportation features and terrain meshes were dynamically adjusted to reduce the projected pixel error. Consequently, the massive terrain and transportation features can be visualized simultaneously in an efficient way. This approach is illustrated with several snapshots, and the efficiency of the algorithms is also demonstrated.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.102
GPT teacher head0.393
Teacher spread0.291 · 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