Tandem Filter Bank-DFT Code for Bursty Erasure Correction
Why this work is in the frame
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Bibliographic record
Abstract
DFT encoding over the real (or complex) field has been proposed as a means to reconstruct samples lost in wireless network transmissions. The quantization associated with practical implementations results in reconstruction errors that are particularly large when lost samples occur in bursts (bursty erasures). Here we present a setup that effectively creates disjoint DFT codes along the rows (temporal codevectors) and columns (subband codevectors) of a frame under analysis. An erasure burst along a particular codevector can then be broken up by reconstructing some lost samples along the remaining orientation; these samples can then be used as received samples in reconstructing the original codevector, a technique that we refer to as pivoting. Expressions for the temporal code reconstruction error and for temporal-to-subband pivoting operations are presented.
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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