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
It its well-known that flexibility and error resilience are significantly improved by employing a scalable bit stream. The major drawback of multi-layered representations within a motion compensated (MC) discrete cosine transform (DCT) based framework is the increase in bit rate as compared to a single-layered representation having the same frequency, spatial and temporal resolution as in the highest layer of the multi-layered representation. This increase in bit rate is due to side information overhead, variable-length coding inefficiencies, and the differing statistics of the error signal. Consequently, much of the research in the area of scalability has focused on non MC-DCT based techniques having inherently scalable properties, e.g. sub-band techniques. However, the ubiquity of MC-DCT based technology suggests that we also address the problem within the MC-DCT framework. This is further warranted given the inclusion of syntax extensions to support scalable coding within newer MC-DCT based video coding standards. In this paper we present a rate-distortion optimized SNR and spatially scalable framework for MC-DCT based video coding.
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 | 0.000 |
| 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