Empowering the creative user
Bibliographic record
Abstract
This paper presents the design, implementation, and validation of a novel system that supports streaming and playout of personalized, multi-path, nonlinear video. In contrast to regular video, in which the file content is played sequentially, our design allows multiple nonlinear video sequences of the underlying (linear) video to be stitched together and played in any personalized order, and clients can be provided multiple path choices. The design combines the ideas of HTTP-based adaptive streaming (HAS) and multi-path nonlinear video. Personalization of the content is achieved with the use of a customized metafile, which is downloaded separately from the underlying media and the manifest file that defines the HAS structure. An extension to the user interface allows path choices to be presented to and made by the user. Novel buffer management and prefetching policies are used to ensure seamless uninterrupted playback regardless of client path choices, even under scenarios in which clients defer their choices until the last possible moment. Our solution allows creative home users to easily create their own multi-path nonlinear video, opening the door to an endless possibility of new opportunities and media forms.
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.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".