Polar thermospheric responses to geomagnetic effects
Bibliographic record
Abstract
Korea Polar Research Institute has been observing high latitude thermosphere winds and temperatures at Northern hemisphere using ground-based Fabry-Perot Interferometers (FPIs) since 2016. As polar thermosphere directly interact with magnetosphere-ionosphere system via the Earth's magnetic field, it is necessary to understand how geomagnetic activities can change physical properties in polar thermosphere and ionosphere. In this study, we use two FPIs located at Resolute Bay, Canada and Kiruna, Sweden representing for polar cap and sub-auroral region, respectively for studying how thermospheric winds and temperatures differently react to geomagnetic activities. National Center for Atmospheric Research Thermosphere Ionosphere Electrodynamic General Circulation Model (NCAR TIEGCM) is used to evaluate the model capacity in high latitude dynamics under different geomagnetic conditions. While TIEGCM agrees very well with FPI thermospheric wind observation inside polar cap, it shows a notable discrepancy near the auroral oval boundary. From the both FPI temperature measurements, we find that thermospheric temperatures are about 100 K higher under active geomagnetic condition than those under quiet condition. Kiruna FPI wind observations are compared with the incoherent scatter radar measurement from 15-23 December 2022 to find how thermosphere-ionosphere dynamically interact each other.
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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".