GoLD
Bibliographic record
Abstract
This paper presents a new technique for fast, view-dependent, real-time visualization of large multiresolution geometric models with color or texture information. This method uses geomorphing to smoothly interpolate between geometric patches composing a hierarchical level-of-detail structure, and to maintain seamless continuity between neighboring patches of the model. It combines the advantages of view-dependent rendering with numerous additional features: the high performance rendering associated with static preoptimized geometry, the capability to display at both low and high resolution with minimal artefacts, and a low CPU usage since all the geomorphing is done on the GPU. Furthermore, the hierarchical subdivision of the model into a tree structure can be accomplished according to any spatial or topological criteria. This property is particularly useful in dealing with models with high resolution textures derived from digital photographs. Results are presented for both highly tesselated models (372 million triangles), and for models which also contain large quantities of texture (200 million triangles + 20 GB of compressed texture). The method also incorporates asynchronous out-of-core model management. Performances obtained on commodity hardware are in the range of 50 million geomorphed triangles/second for a benchmark model such as Stanford's St. Matthew dataset.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.371 | 0.238 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".