Bibliographic record
Abstract
for his time and help. I thank CMC Microsystems for donating FPGA design resources and equipment to McGill University. I thank Altera Corporation for giving me the opportunity to present my work. I thank Tsuyoshi Hamada, Naohito Nakasato, and Dr. Ebisuzaki of the RIKEN Institute for sponsoring my visit. Finally, I thank my family and friends. 1 Particle graphics simulations are well suited for modeling phenomena such as water, cloth, explosions, fire, smoke, and clouds. They are normally realized in software, as part of an interactive graphics application. Their use in such applications is lim-ited by the computational burden and resource competition they create. This thesis presents the design and implementation of a reconfigurable hardware particle graph-ics system for accelerating real-time particle graphics effects: The Particle Pipe. We explore the design process, implementation issues, limitations, challenges, and new possibilities of using FPGAs for the acceleration of real–time particle graphics. The
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.294 | 0.195 |
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".