High-latitude ionospheric response to co-rotating interaction region- and coronal mass ejection-driven geomagnetic storms revealed by GPS tomography and ionosondes
Bibliographic record
Abstract
Positive ionospheric anomalies induced in the polar cap region by co-rotating interaction region (CIR)- and coronal mass ejection (CME)-driven geomagnetic storms are analysed using four-dimensional tomographic reconstructions of the ionospheric plasma density based on measurements of the total electron content along ray paths of GPS signals. The results of GPS tomography are compared with ground-based observations of F region plasma density by digital ionosondes located in the Canadian Arctic. It is demonstrated that CIR- and CME-driven storms can produce large-scale polar cap anomalies of similar morphology in the form of the tongue of ionization (TOI) that appears on the poleward edge of the mid-latitude dayside storm-enhanced densities in positive ionospheric storms. The CIR-driven event of 14–16 October 2002 was able to produce ionospheric anomalies (TOI) comparable to those produced by the CME-driven storms of greater Dst magnitude. From the comparison of tomographic reconstructions and ionosonde data with solar wind measurements, it appears that the formation of large-scale polar cap anomalies is controlled by the orientation of the interplanetary magnetic field (IMF) with the TOI forming during the periods of extended southward IMF under conditions of high solar wind velocity.
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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".