Interplanetary magnetic field control and magnetic conjugacy of auroral <i>E</i> region backscatter
Bibliographic record
Abstract
The interplanetary magnetic field (IMF) control and magnetic conjugacy of the auroral E region backscatter are assessed using observations by the nominally conjugate Super Dual Auroral Radar Network (SuperDARN) Syowa East and Pykkvibaer HF radars at ∼69° magnetic latitude and the ACE satellite measurements in the solar wind. A common‐mode radar data set comprising 118 days in January–December 2000 is considered, and the E region echo occurrence rates are calculated for each 10 min interval. The occurrence variations are adjusted for the magnetic local time and seasonal dependencies by subtracting the reference daily trends for low IMF magnitudes. The Bz effects dominate at Bz < 0, while By effects become noticeable at Bz > 0. The occurrence strongly increases as Bz becomes more negative and minimizes at small negative By and positive Bz values. The Bz effects are stronger by a factor of ∼2 than those of By. The extent of magnetic conjugacy is considerable, with an overall correlation between conjugate occurrences of 0.7. The backscatter is most conjugate at large negative Bz and generally at large IMF values. The correlations are higher on the dayside and during the equinoxes, indicating a preference for both conjugate locations to be sunlit. Finally, correlations of occurrences with solar wind coupling functions are higher on the dayside, with some evidence of the response time increasing away from magnetic noon, implying more direct IMF control near noon. The overall predictability of the E region backscatter occurrence from the solar wind inputs is considerable, with correlations being higher than those reported previously for the F region backscatter.
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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 | 0.000 |
| 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".