Visual specification of behaviours in VRML worlds
Bibliographic record
Abstract
The Virtual Reality Modeling Language (VRML) is a textual language used to define objects in 3D worlds, and their behaviours. It is used extensively for providing 3D views and simulations on the internet, through the use of VRML plug-ins. As with languages like HTML or XML, VRML is expressed textually. Although there are a variety of tools allowing for the graphical, interactive definition of 3D objects which can then be exported as VRML text files, definitions of behaviours must be specified textually, generally through short programs written in a scripting language such as JavaScript, connected to the 3D objects using declarations in the VRML file.We are investigating the use of visual programming techniques in an attempt to make it possible for a broader class of users to be able to make use of behaviours in VRML. In this paper we describe our work on showing visual connections between objects, the scripts which control their behaviours, and the objects which control these scripts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".