Bibliographic record
Abstract
Multimedia distribution over the Internet is becoming increasingly popular. A novel framework for Internet video streaming is proposed. For video compression, our previously developed three-dimensional significance-linked connected component analysis (3D-SLCCA) codec is applied. 3D-SLCCA provides high coding efficiency, multiresolution video representation, transmission error resilience, and low computational complexity. For audio coding, the GSM standard is used. For error control, retransmission and error concealment are jointly applied. Multiresolution-multicast transmission is implemented by assigning different multicast group addresses to different video layers. Thus each receiver subscribes to the maximum number of layers that both its hardware resource and network capability can handle. By using a hierarchically structured multicast tree, each node is responsible for caching packets, collecting NACK packets, and sending repair packets. This not only significantly reduces the latency, but also efficiently solves the "ACK implosion" problem. As opposed to data transmission, reliable multicast is not required by the network infrastructure. Based on timing constraint and importance of lost packets, each receiver decides whether to request retransmission or apply error concealment. Finally, synchronization is accomplished by using the timestamp mechanism of RTP.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".