Bibliographic record
Abstract
Oversampled blind maximum likelihood sequence detection and estimation (MLSDE) is proposed to improve the performance and robustness of the MLSDE receiver. The performance of the MLSDE receiver depends firmly on the assumed channel model and available knowledge about the channel parameters at the receiver. A deterministic channel model is used to enhance the robustness of the receiver in a dynamic environment such as mobile communications. Oversampled joint channel estimation and data detection is proposed to improve the receiver performance which approaches the performance of the receiver knowing the channel parameters completely. The estimation part of the oversampled MLSDE leads to an RLS-type algorithm for additive colored Gaussian noise which becomes the noise model in the oversampling method. Bandwidth efficiency is achieved by using blind channel estimation (i.e. no training sequence) and two-level differentially encoded quadrature phase shift keying modulation scheme with 16 points constellation (16-DQPSK).
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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 | 0.000 | 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) | 0.001 | 0.001 |
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".