Desirable Elements for a Particle System Interface
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
Particle systems have many applications, with the most popular being to produce special effects in video games and films. To permit particle systems to be created quickly and easily, Particle System Interfaces (PSIs) have been developed. A PSI is a piece of software designed to perform common tasks related to particle systems for clients, while providing them with a set of parameters whose values can be adjusted to create different particle systems. Most PSIs are inflexible, and when clients require functionality that is not supported by the PSI they are using, they are forced to either find another PSI that meets their requirements or, more commonly, create their own particle system or PSI from scratch. This paper presents three original contributions. First, it identifies 18 features that a PSI should provide in order to be capable of creating diverse effects. If these features are implemented in a PSI, clients will be more likely to be able to accomplish all desired effects related to particle systems with one PSI. Secondly, it introduces a novel use of events to determine, at run time, which particle system code to execute in each frame. Thirdly, it describes a software architecture called the Dynamic Particle System Framework (DPSF). Simulation results show that DPSF possesses all 18 desirable features.
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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 | 0.002 | 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