Seasonal and Diurnal Dynamics of Radio Noise for 8–20 MHz Poleward‐Oriented Mid‐Latitude Radars
Bibliographic record
Abstract
Abstract Based on ray tracing in a smooth ionosphere described by the IRI‐2012 model we have inferred the seasonal‐diurnal dynamics of radio noise observed by four mid‐latitude high‐frequency (HF) radars. In the calculations, noise is assumed to be homogeneous and stationary, but the main contribution comes from the radar skip zone boundary due to focusing radiowaves effect. Noise absorption along the ray path is simulated from the IRI‐2012 electron density, and from the molecular nitrogen density and electron temperatures obtained from the NRLMSISE‐00 model. Earth magnetic field is not taken into account both in the absorption and ray‐tracing calculations due to insufficient accuracy of the ionospheric model. The model results are compared with experimental radar data, and good agreement between the two is demonstrated. It is shown that experimentally observed seasonal and diurnal dynamics of the noise correlates well with model predictions. We demonstrated saturation effect at low noise levels. The model makes it possible to estimate the amount of absorption in D‐ and E‐layers using noise observations at SuperDARN and SuperDARN‐like poleward‐oriented radars, especially at mid‐latitudes. This is important for the retrieval of long term variations in the electron density in the lower ionosphere, by using wide coverage provided by these radars' network. The model also makes it feasible to interpret vertical absorption by experimental noise observations, thereby significantly expanding the capability of HF radars to monitor the lower ionosphere, and to provide data for joint analysis with other data, obtained by these radars at E‐ and F‐layer heights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".