Multi‐instrument, high‐resolution imaging of polar cap patch transportation
Bibliographic record
Abstract
Abstract Transionospheric radio signals in the high‐latitude polar cap are susceptible to degradation when encountering sharp electron density gradients associated with discrete plasma structures, or patches. Multi‐instrument measurements of polar cap patches are examined during a geomagnetic storm interval on 22 January 2012. For the first time, we monitor the transportation of patches with high spatial and temporal resolution across the polar cap for 1–2 h using a combination of GPS total electron content (TEC), all‐sky airglow imagers (ASIs), and Super Dual Auroral Radar Network (SuperDARN) HF radar backscatter. Simultaneous measurements from these data sets allow for continuous tracking of patch location, horizontal extent, and velocity despite adverse observational conditions for the primary technique (e.g., sunlit regions in the ASI data). Spatial collocation between patch‐like features in relatively coarse but global GPS TEC measurements and those mapped by high‐resolution ASI data was very good, indicating that GPS TEC can be applied to track patches continuously as they are transported across the polar cap. In contrast to previous observations of cigar‐shaped patches formed under weakly disturbed conditions, the relatively narrow dawn‐dusk extent of patches in the present interval (500–800 km) suggests association with a longitudinally confined plasma source region, such as storm‐enhanced density (SED) plume. SuperDARN observations show that the backscatter power enhancements corresponded to the optical patches, and for the first time we demonstrate that the motion of the optical patches was consistent with background plasma convection velocities.
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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 | 0.001 | 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".