Polar Ionospheric Large‐Scale Structures and Dynamics Revealed by TEC Keogram Extracted From TEC Maps
Bibliographic record
Abstract
Abstract A method named total electron content (TEC) keogram is introduced for surveying the large‐scale irregularities continuously in the polar ionosphere. The TEC keogram is developed from a movie of TEC maps along various meridian lines from the dayside to the nightside across the magnetic pole, trying to identify and track several types of ionospheric structures. Through two examples, a clear train of polar cap patches are identified from TEC keogram and confirmed by SuperDARN radar observations. The motion speed of these patches estimated from this tool agrees with SuperDARN radar measurements. Then, the motions of patches relative to the background convection through the whole polar cap are statistically studied for the first time. Moreover, the occurrence dependence of fully tracked patches on months, UT hours, and interplanetary magnetic field conditions is generally consistent with previous reports. These results suggest that the TEC keogram offers a power tool for continuous monitoring and studying of large‐scale plasma irregularities in the polar 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 | 0.002 | 0.001 |
| 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".