Xr: Crossdevice Rendering for Vector Graphics. 2003 ottawa linux symposium
Bibliographic record
Abstract
Xr provides a vector-based rendering API with output support for the X Window System and local image buffers. PostScript and PDF file output is planned. Xr is designed to produce identical output on all output media while taking advantage of display hardware acceleration through the X Render Extension. Xr provides a stateful user-level API with support for the PDF 1.4 imaging model. Xr provides operations including stroking and filling Bézier cubic splines, transforming and compositing translucent images, and antialiased text rendering. The PostScript drawing model has been adapted for use within C applications. Extensions needed to support much of the PDF 1.4 imaging operations have been included. This integration of the familiar PostScript operational model within the native application language environment provides a simple and powerful new tool for graphics application development. The design of the Xr library is motivated by the desire to provide a high-quality rendering interface for all areas of application presentation, from labels and shading on buttons to the central image manipulation in a drawing or painting program. Xr targets displays, printers and local image buffers with a uniform rendering model so that applications can use the same API to present information regardless of the media. The Xr library provides a device-independent API, and can currently drive X Window System[10] applications as well as manipulate images in the application address space. It can take advantage of the X Render Extension[7] where available but does not require it. The intent is to add support for Xr to produce PostScript[1] and PDF 1.4[5] output. Moving from the primitive original graphics system available in the X Window System to a complete device-independent rendering environment should serve to drive future application development in exciting directions. 1.1 Vector Graphics 1
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.287 | 0.209 |
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".