Context-based media adaptation in pervasive computing
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
In pervasive computing applications, computer embedded devices are used to perform control tasks and access the Internet or other information sources. However, most content of current information sources was designed with desktop computers and high-speed network connections in mind. They contain rich media data, which are not suitable for those pervasive devices with limited capabilities. Moreover, for different user preference and application scenarios, not all of the data are relevant and critical to the application. Context-based media adaptation is mainly concerned with selecting different qualities of single media types or selecting different media types, and then delivering information to different context. Some related issues for building a general context-based media adaptation framework are discussed. These issues include context description and exchange schemes, adaptable model for managing and manipulating multimedia content, adaptation techniques and architectural issues.
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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 | 0.001 |
| Open science | 0.000 | 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