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
We propose an error-resilient multiple-description (MD) video coding algorithm based on three-dimensional (3D) set partitioning in hierarchical trees (SPIHT). In this approach, the wavelet transform coefficients are divided into multiple independent substreams that are separately transmitted over error-prone networks. Compared to other MD video coders based on 3D SPIHT, the novelty of the proposed approach is the injection of additional redundancy into the substreams so that the coefficients in the spatial root subband are protected highly during the transmission. As a consequence, spatial root subband coefficients that are missing due to transmission errors are recovered at the decoder with a high accuracy. Simulation results on different video sequences show that the proposed method maintains error resilience with high coding efficiency. In particular, our results demonstrate that the proposed algorithm achieves a significant improvement on video quality by up to 3.35 dB in the presence of a substream loss compared to the existing MD video coders that use 3D SPIHT.
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 | 0.001 |
| 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