Visualizing Massive Terrain with Transportation Infrastructure by Using Continuous Level of Detail
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.
Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it