Bibliographic record
Abstract
/ / Creating a hardware accelerated image with variable / / alpha transparencies. BufferedImage imageAlpha = graphicConf. createCompatibleImage(x,y, Transparency.TRANSLUCENT); Rendering to the Screen As previously mentioned, images should first be drawn on the memory buffer. / / Drawing an image on the memory buffer graph2D.drawImage(bufferedImage, x, y, null); Once the frame is ready, we can render the content of the memory buffer to the screen. / / Rendering the current buffer to the screen graph2D.dispose(); bufferStrategy.show(); graph2D = (Graphics2D)bufferStrategy.getDrawGraphics(); Conclusion and Future Work Despite our initial apprehension about using Java for game development, Java 2D has shown to be very efficient, if used properly. Two years of using Minueto for the game development of COMP-361 projects, the systems development project course at McGill, has shown that it performs extremely well, thanks to the different optimizations presented in this article. Our future work in this area will focus on efficient ways to combine Swing and Java 2D technology.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.366 | 0.250 |
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".