Modeling and Validating a SuperDARN Radar's Poynting Flux Profile
Bibliographic record
Abstract
Abstract We have developed a model that simulates the Poynting flux profile of the Saskatoon Super Dual Auroral Radar Network (SuperDARN) radar at ionospheric altitudes. The model uses ray tracing software to project the radar system's vacuum Poynting flux profile through the ionosphere, taking into account the influence of the ionospheric medium on the propagation characteristics of the high frequency radio waves. Measurements of the radar's transmissions by the Radio Receiver Instrument (RRI) in low‐Earth orbit are used to validate the model during five experiments which occurred between 4 and 8 August 2017. Comparisons between simulated and measured RRI antenna voltages show good agreement, although there are clear instances in which the model underperforms. Nevertheless, the model demonstrates its utility as a tool for interpreting RRI measurements of SuperDARN radars. The model also helps address a lack of knowledge of a SuperDARN radar's Poynting flux profile at ionospheric altitudes. In particular, we assess the assumption that SuperDARN's scattering volume lies along the great‐circle path of the transmitting beam's bearing. Comparisons between the model and RRI's measurements show that this assumption is reasonable for the five experiments investigated in this work. The model presents a new way of carrying out SuperDARN and high frequency radio science investigations.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".