<title>CaML: Camera Markup Language for Network Interaction</title>
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
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 <i>provider of pixels</i>, but as a <i>video information server</i>. 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.
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.
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 | 0.001 | 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