<title>CaML: Camera Markup Language for Network Interaction</title>
Bibliographic record
Abstract
As processor speeds increase and the cost of digital video technology falls, the use of video is expanding in a plethora of applications including video surveillance, human computer interaction, tele-instruction, and enhanced sports broadcasts. However, a major problem that now faces developers of video systems is the requirement to build the low-level video processing from the ground up for each application. This paper describes a camera system that acts not merely as a provider of pixels, but as a video information server. A video application interacts with the camera server using the Camera Markup Language (CaML, pronounced camel) proposed here. CaML is an XML-based (Extensible Markup Language) data format for exchanging video information with a server. It provides a layer of abstraction between the application and the pixels to simplify the development process and is well-suited to exchanging data over a network. Using a camera as a server on a network makes it a simple matter for a single application to use multiple cameras. Local- and wide-area networks (LANs and WANs) replace the need for conventional methods for routing video signals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.320 | 0.384 |
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".