Improving the Magic constant – data-based calibration of phased array radars
Bibliographic record
Abstract
Abstract. We present a method for improved calibration of multi-point electron density measurements from incoherent scatter radars (ISR). It is based on the well-established Flatfield correction method used in imaging and photography, where we exploit the similarity between independent measurements in separate pixels in an image sensor and multi-beam radar measurements. Applying this correction method adds to the current efforts of estimating the magic constant or system constant made for the calibration of multi-point radars, increasing data quality and usability by correcting for variable, unaccounted, and unpredictable variations in system gain. This second-level calibration is especially valuable for studies of plasma patches, irregularities, turbulence, and other research where inter-beam changes and fluctuations of electron density are of interest. The method is strictly based on electron density data measured by the individual radar and requires no external input. This is of particular interest when independent measurements of electron densities for calibration are available only in one pointing direction or not at all. A correction factor is estimated, which is subsequently used to scale the electron density measurements of a multi-beam ISR experiment run on a phased array radar such as RISR-N, RISR-C, PFISR, or the future EISCAT3D radar. This procedure could improve overall data quality if used as part of the data-processing chain for multi-beam ISRs, both for existing data and for future experiments on new multi-beam radars.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".