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 exploit the similarity between source compression and channel decoding to develop a new encoding algorithm for trellis vector quantization (TVQ). We start by drawing the analogy between TVQ and the process of sequence-ML channel decoding. Then, the new search algorithm is derived based on the symbol-MAP decoding algorithm, which is used in soft-output channel decoding applications. Given a block of source output vectors, the new algorithm delivers a set of probabilities that describe the reliability of the different symbols at the encoder output for each time instant, in the minimum distortion sense. The performance of both the new algorithm and the Viterbi algorithm is compared using memoryless Gaussian and Gauss-Markov sources. The two algorithms provide expected similar distortion-rate results. This behavior is due to the fact that sequence-ML decoding is equivalent to symbol-MAP decoding of independent and identically distributed data symbols. Although the new algorithm is approximately 4 times more complex than the Viterbi (1974) algorithm, it provides distortion-dependent reliability information that can be used to improve the quality of compression.
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