Bibliographic record
Abstract
In this paper, we use an enhanced surface mesh model to simulate virtual dissection by progressive subdivision and re-meshing. Enhanced novel algorithms to generate interior structures that show the result of a cut that is generated by the interaction between instrument and model is used as the underlining framework. The notion of a cross cutting is introduced and solved to offer a more realistic interactive environment for various virtual diagnostic and dissection tasks. Our simulator supports two types of cutting: "cut-into", an instrument penetrating simulated tissues, and "cut through", an instrument cutting through tissues. In either case, a groove is developed in the path where the cutting has taken place to reflect the depth of the cut. Generation of a groove introduces a challenging problem when two cuts cross in their paths. For example, when two cutting path intersects (i.e., an occurrence of cross cutting), the current structures involved in the proximity of the cuts do not simulate a realistic intersecting cross cut. Although many studies have represented solutions to surface mesh cutting, a solution to an interactive cross cutting has not been adequately addressed yet. This paper presents an algorithm that can be flexibly applied for the simulation of an interactive cross cutting on a surface-mesh. Such solution can be further applied to many applications involving surface mesh.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".