Statistical estimates of auroral Pedersen conductance using electric and magnetic measurements by the Swarm spacecraft
Bibliographic record
Abstract
Height-integrated ionospheric Pedersen and Hall conductances play a major role in ionospheric electrodynamics and Magnetosphere-Ionosphere coupling. Especially the Pedersen conductance is a crucial parameter in estimating ionospheric energy dissipation via Joule heating. Unfortunately, the conductances are rather difficult to measure directly in extended regions, so statistical models and various proxies are often used. We discuss a method for estimating the Pedersen Conductance from magnetic and electric field data provided by the Swarm satellites. We need to assume that the height-integrated Pedersen current is identical to the curl-free part of the height integrated ionospheric horizontal current density, which is strictly valid only if the conductance gradients are parallel to the electric field. This may not be a valid assumption in individual cases but could be a good approximation in a statistical sense. Further assuming that the cross-track magnetic disturbance measured by Swarm is mostly produced by field-aligned currents and not affected by ionospheric electrojets, we can use the cross-track ion velocity and the magnetic perturbation to directly estimate the height-integrated Pedersen conductance. We present initial results of a statistical study utilizing 5 years of data from the Swarm-A and Swarm-B spacecraft, and discuss possible applications of the results and limitations of the method.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".