Ionospheric response to the corotating interaction region–driven geomagnetic storm of October 2002
Bibliographic record
Abstract
Unlike the geomagnetic storms produced by coronal mass ejections (CMEs), the storms generated by corotating interaction regions (CIRs) are not manifested by dramatic enhancements of the ring current. The CIR‐driven storms are however capable of producing other phenomena typical for the magnetic storms such as relativistic particle acceleration, enhanced magnetospheric convection and ionospheric heating. This paper examines ionospheric plasma anomalies produced by a CIR‐driven storm in the middle‐ and high‐latitude ionosphere with a specific focus on the polar cap region. The moderate magnetic storm which took place on 14–17 October 2002 has been used as an example of the CIR‐driven event. Four‐dimensional tomographic reconstructions of the ionospheric plasma density using measurements of the total electron content along ray paths of GPS signals allow us to reveal the large‐scale structure of storm‐induced ionospheric anomalies. The tomographic reconstructions are compared with the data obtained by digital ionosonde located at Eureka station near the geomagnetic north pole. The morphology and dynamics of the observed ionospheric anomalies is compared qualitatively to the ionospheric anomalies produced by major CME‐driven storms. It is demonstrated that the CIR‐driven storm of October 2002 was able to produce ionospheric anomalies comparable to those produced by CME‐driven storms of much greater Dst magnitude. This study represents an important step in linking the tomographic GPS reconstructions with the data from ground‐based network of digital ionosondes.
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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) | 0.001 | 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".