Interplanetary magnetic field control of polar patch velocity
Bibliographic record
Abstract
Polar patch drift speed and direction have been investigated using oxygen 630 nm images recorded by an all‐sky imager at Eureka (89° CGM), Canada over an extended period, January and December 1998. Statistical studies showed that (1) the interplanetary magnetic field (IMF, from the WIND satellite) Bz component linearly controls the patch speed when Bz is between −7.5 nT and 0 nT. The speed tends to saturate when Bz is less than −7.5nT due to nonlinear coupling between the solar wind and the magnetosphere. The average patch speed of 600 m/s is in agreement with results from earlier studies; (2) The IMF By or the IMF clock angle has a clear control of the patch drift direction as determined by the drift azimuth angle. When ∣By∣ is less than 7.5nT, the drift azimuth angle is linearly and positively correlated with By. For a large ∣By∣ (>7.5 nT), the positive correlation is replaced by a negative correlated linear relation and the azimuth angle tends to turn towards 180 degrees; that is, the patches drift in an antisunward direction. These IMF By effects can be qualitatively explained by the northern winter polar ionospheric convection models developed by Weimer [1995] and Hairston and Heelis [1990] . Results from our quantitative study on the IMF control of polar patch speed and drift direction provide constraints for the development of future polar ionospheric convection models.
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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 | 0.000 | 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".