Ancestor based survivor decision in M-algorithm convolutional decoding
Bibliographic record
Abstract
In this paper, we propose a new algorithm for the surviving path decision in M-algorithm convolutional decoders. Correct path loss introduces one of the most destructive effects on the M-algorithm which mostly leads to catastrophic error. In the proposed M-algorithm survivor decision scheme, the M surviving states are not selected solely based on their own path metric. Among the M surviving states, some states with the best path metrics survive and also some other states whose ancestors have had the best path metrics survive. This way of survivor selection enables us to avoid correct path loss if an abrupt noise deteriorates the correct path metric severely. Simulation results show that the error rate performance of the ancestor-based survivor decision in some cases is slightly better than currently-best survivor decision scheme in the presence of the additive white Gaussian noise (AWGN). However, it is expected that the proposed algorithm offer better performance than the conventional methods in the presence of abrupt noise (short-term high value noise) like shot noise or over fading channel
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".