Technique: Large‐scale ionospheric conductance estimated from combined satellite and ground‐based electromagnetic data
Bibliographic record
Abstract
A technique for estimating the large‐scale spatial distribution of the height‐integrated electrical conductivity (conductance) of the Earth's high‐latitude ionosphere is presented. By combining satellite magnetic perturbation data with ground‐based magnetometer and HF radar observations the Hall (Σ H ) and Pedersen (Σ P ) conductances are independently calculated. Magnetic perturbations observed at the Earth's surface are combined with those recorded by satellites to calculate the horizontal ionospheric current ( ⊥ ). Combined HF radar and satellite ion drift data give the ionospheric electric field ( ⊥ ), which combined with ⊥ allows Σ H and Σ P to be estimated from Ohm's law. Conductance results from preliminary application of the technique to an event with a 1 hour integration time are presented. Significant uncertainties arise due to sparse ground magnetometer and electric field coverage. The resulting conductance distribution shows an enhancement in the auroral regions as expected from a statistical model conductance. However, the calculated conductance enhancement is located between the region 1 and region 2 Birkeland currents. This agrees with the statistical model data in the morning sector but disagrees in the evening sector. Comparison of the conductance results with conductance data inferred from in situ precipitating particle observations shows colocated enhancements. The calculated data also show a low (<1) Hall to Pedersen conductance ratio in regions near downward Birkeland current.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".