An Adaptive Multiresolution Method for Progressive Model Transmission
Bibliographic record
Abstract
Although there are many adaptive (or view-dependent) multiresolution methods, support for progressive transmission and reconstruction has not been addressed. A major reason for this is that most of these methods require a large portion of the hierarchical data structure to be available at the client before rendering starts. This is due to the dependency constraints among neighboring vertices. In this paper, we present an efficient, adaptive, multiresolution method that allows progressive and selective model transmission. It is achieved by reducing the neighboring dependency to a minimum. The new method allows visually important parts of an object to be transmitted to the client at higher priority than the less important parts, and progressively reconstructed there for display. It is even possible to transmit only the visible parts of a model and reconstruct these visible parts at the client. The ability to selectively transmit allows the visualization of very large models across the network with minimal delay. We will present how our method works in a client-server environment. We will also show the data structure of the transmission record and some performance results of the method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".