Consistent estimation models for the fine time synchronization of FH systems
Why this work is in the frame
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Bibliographic record
Abstract
Four basic estimation methods are discussed, namely the delay-advance method, the early-late filter method, a modified early-late filter method, and the novel two-tone method. These methods all use noise power estimation in a frequency bin not occupied by a signal; this estimate is then used to reduce the bias and make the estimate of the time error consistent. Simulation results on the performance of these four methods obtained using the bias reduction (BR) technique are given. The simulation results are for AWGN (additive white Gaussian noise) only, which represents system noise and noise jamming across the entire band. The BR technique is shown to reduce the bias and make the estimates consistent for all four basic methods. Although the BR also increases the standard deviation, the decrease in bias more than offsets the effect of increased standard deviation. The best overall performance is achieved with the modified early-late filter method.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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