Impacts of Subauroral Polarization Streams on Storm-Enhanced Density and Tongue of Ionization
Bibliographic record
Abstract
The influences of subauroral polarization streams (SAPS) on storm-enhanced density (SED) and tongue of ionization (TOI), an important topic in the field of magnetosphere-ionosphere-thermosphere coupling, however, remain undetermined. The Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM) with/without an empirical SAPS model has been used to investigate the impacts of SAPS on SED and TOI. The modeled TEC and ion drift velocities agree reasonably well with the observations of GNSS and DMSP satellites on 17 March 2013. The TIEGCM simulations show that SAPS can significantly affect the electron density of SED and TOI depending on the relative location of SAPS and SED. SAPS reduces the electron density at the eastward edge of SED where they are overlapped, and enhances SED at its westward edge. A term-by-term analysis of the O+ ion continuity equation in the F-region shows that the electron density depletions at the eastward edge of SED are mainly due to increased local plasma loss rates because of SAPS elevated plasma-neutral temperatures and O/N2 reduction because of thermosphere upwelling. The electron density enhancements in the westward edge of SED are mainly due to SAPS-induced westward plasma E×B transports and O/N2 increment because of thermospheric downwelling. Moreover, SAPS-induced electron depletions in the throat region weaken TOI as plasmas undergo anti-sunward convection into the polar cap.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".