Future traffic demands and characteristics from a media perspective
Bibliographic record
Abstract
Providing topical information and entertainment began with wall paintings, the spoken word and face-to-face performance, then the addition of the written and printed word along with illustrations and pictures, followed by audio recording. In the early 1920s, regular broadcast radio services began, followed by television in the late 1930s, and this has provided the basis of broadcast media we know today. These innovations frequently pushed boundaries and challenged the status quo, but not all of these challenges were technical by any means. However, it could be argued that the development of accessible technologies has been fundamental to the successful deployment of information and entertainment media in all their forms throughout history. Today, the merging of audio and video media with a whole range of digital services is becoming commonplace. With the ability of such services to develop new approaches in supporting people's everyday living experiences, this will take communication networks into a new era central to the way we live. This paper postulates that the historical trends with audio and video media developments from the early 1900s will continue to push future boundaries, and attempts to highlight the key demands and the developing trends from a communication network point of view.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".