Causal source coding of stationary sources with high resolution
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
Neuhoff and Gilbert (1982) defined a causal lossy source code as a system where the reconstruction of the present source sample is restricted to be a function of the present and past source samples, while the code stream itself may be non-causal and have variable rate. They showed that for stationary and memoryless sources, optimum causal source coding is achieved by time-sharing at most two entropy coded scalar quantizers. We extend this result to general real valued stationary sources with finite differential entropy rate, in the limit of small distortions. We show that for the mean square distortion, the optimum causal encoder at high resolution is a fixed uniform quantizer followed by a sequence entropy coder. Thus, the cost of causality is the "space filling loss" of the uniform quantizer, i.e., (1/2)log(2/spl pi/e/12)/spl ap/0.254 bit. This generalizes the well known result of Gish and Pierce on asymptotically optimal entropy constrained scalar quantization.
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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 | 0.000 | 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