Bibliographic record
Abstract
Abstract Radio channels are usually modeled as a well‐defined Rice process. The statistics of its wrapped phase (i.e., phase values in [−π,π)), such as the mean, variance, and probability density function (pdf), are known. The absolute phase, i.e., the accumulated phase change over an observation interval, is considered here as a new channel variable. Its use for channel characterization can extend to cognitively track users. However, there is very little knowledge about the statistics of the absolute phase. In fact, the known, associated effects of the absolute phase, such as various click noise contributions, are not consistently treated or interpreted in the literature. The definitions of absolute phase, based on both unwrapping and on other methods previously discussed for FM receivers, lay a basis for analysis of the mean, variance, and pdf of the absolute phase for a well‐defined Rice process. The conditions are identified for approximate pdf models to hold, and it is noted that pdfs for the absolute phase for small or medium Rice factor and small observation interval are open problems. Simulations are used to support the analysis and discussion. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".