A Statistical Examination of the Spatial Correlations of HF Ionospheric Absorption Signatures Using the GO‐Canada Riometer Network
Bibliographic record
Abstract
Abstract A network of 30‐MHz riometers distributed across Canada have been monitoring auroral absorption for decades. Electron precipitation can cause enhanced densities to develop below ∼100 km where the higher neutral density can cause absorption of High Frequency (HF) signals. Modeling D‐region absorption accurately can be challenging due to the large distances between riometer monitoring sites. It is of interest to develop an understanding of the typical scale sizes of these regions for assimilation in ionospheric models. Using the entire network of GO‐Canada riometers, auroral absorption events may be monitored as they develop through a wide MLT sector. By examining correlations between sites, proxies for the scale sizes of absorption regions may be estimated. It was found that the sizes of the absorbing regions in the zonal direction are on the order of 900 km. Conversely, the characteristic scale sizes of the regions in the meridional direction were ∼700 km. These results are consistent with the general magnetospheric structure which sees higher energy electrons primarily present in the nightside transition region, and ring current. This creates a natural limit in latitudinal correlations, while the spread in longitude is likely connected to the size of precipitation regions in MLT. Our results also show significant spread in scale sizes in both directions from a few hundred to several thousand km. In this paper, we summarize these results in terms of the observed trends in high energy electron precipitation spatial and temporal scales and discuss future work to connect these scales to magnetospheric drivers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".