Bibliographic record
Abstract
A pilot-assisted turbo coding system is proposed in which pilot bits are periodically inserted into the bit stream to be encoded by turbo encoder. In our rate 1/2 example, both the systematic bit for the pilot and a corresponding parity are punctured to keep the code rate unchanged On the decoding side, two schemes are proposed to improve the performance; the first uses the pilots to detect possible errors and scales the extrinsic information accordingly, while the second modifies the path metric. These two ideas can be combined to further improve the performance. The application of this scheme to soft output Viterbi algorithm (SOVA) is studied. In comparison with the standard SOVA decoder, our simulation results show that the use of pilot symbols to detect errors and scale the extrinsic information can result in a coding gain of 0.38 dB at an SNR of 2.5 dB. By itself, the modified metric contributes to an improvement of 0.12 dB at this same SNR, while a combination of the above two schemes achieves a 0.45 dB improvement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".