Bibliographic record
Abstract
Abstract Autocorrelation function (ACF) estimates from voltage data measured by high‐frequency ionospheric radar systems that utilize the multiple‐pulse technique of Farley (1972) are susceptible to interference from self‐clutter. Self‐clutter is caused by simultaneous returns from multiple transmitted pulses echoing from unwanted, or ambiguous ranges. Without accurate estimates of self‐clutter it is impossible to account for all the uncertainty in estimates of the radar ACF. Voltage‐ and power‐based self‐clutter estimators are presented and evaluated using a modified version of the radar data simulator of Ribeiro et al. (2013a) and data from the Super Dual Auroral Radar Network (SuperDARN). It is shown that self‐clutter caused by ambiguous ranges filled with ground scatter can be accurately estimated using a voltage‐based self‐clutter estimator but that for ionospheric origin self‐clutter a maximal estimator must be used. Two maximal self‐clutter estimators are discussed and verified using the radar data simulator. A discussion of the application of the self‐clutter estimator as it is applied to ACFs obtained with Saskatoon SuperDARN radar is also presented.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".