Investigating Spatial and Temporal Structuring of E‐Region Coherent Scattering Regions Over Northern Norway
Bibliographic record
Abstract
Abstract Recently, it has been shown that the Spread Spectrum Interferometric Multistatic meteor radar Observing Network radar system located in northern Norway is capable of measuring ionospheric E‐region coherent scatter with spatial and temporal resolutions on the order of 1.5 km and 2 s, respectively. Four different events from June and July of 2022 are examined in the present study, where the coherent scatter measurements are used as a tracer for large‐scale ionospheric phenomena such as plasma density enhancements and ionospheric electric fields. By applying a two‐dimensional Fourier analysis to range‐time‐intensity data, we perform a multi‐scale spatial and temporal investigation to determine the change in range over time of large‐scale ionospheric structures (>3 km) which are compared with line‐of‐sight velocities of the small scale structures (∼5 m) determined from the Doppler shift of the coherent scatter. The spectral characteristics of the large‐scale structures are also investigated and logarithmic spectral slopes for scale sizes of 100–10 km were found to be between −3.0 and −1.5. This agrees with much of the previous work on the spatial spectra scaling for ionospheric electric fields. This analysis aids in characterizing the source of the plasma turbulence and provides crucial information about how energy is redistributed from large to small scales in the E‐region ionosphere.
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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 | 0.000 |
| 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) | 0.000 | 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".