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
Efficient online 3D visualization is essential for a variety of applications, including not only games and e-commerce, but also heritage and medicine. For efficient online visualization, it is necessary to adapt 3D models (both mesh and texture) quickly to the available computational or network resources. We propose using 3D model simplification based on a scale-space analysis of the surface curvature variations combined with an associated scale-space analysis of the surface texture to reduce the size of texture files, and facilitate distributed transmission. The premise of the proposed simplification is that minor variations in texture can be ignored in relatively smooth regions of a 3D surface, without significantly affecting human perception. Statistics of feature points and their associated texture fragments are gathered during preprocessing. On-line transmission and rendering for the next higher resolution scale is based on the statistics, which can be retrieved in constant time. Quality of service (QoS) can be provided based on the time limit, or the number of vertices, or faces, requested by the viewer. Experimental results showing the simplified models demonstrate the feasibility of our approach.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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