Diagnostics of auroral oval boundaries on the basis of the magnetogram inversion technique
Bibliographic record
Abstract
It is shown that the convection reversal boundary is a fundamental parameter of the magnetosphere-ionosphere coupling, which determines a strong analogy between the electrostatic potential of the ionosphere and the equivalent current function in the dipole geomagnetic field approximation and the uniform ionospheric conductance. We have developed a new ground-based method for automatically diagnosing boundaries of the auroral oval using output data obtained with the magnetogram inversion technique (MIT). Using maps of the current function and field-aligned currents, calculated at the first stage of MIT with uniform ionospheric conductance, we determine the convection reversal boundary, polar cap boundary, equatorial boundary of the auroral oval, and line of maximum density of auroral electrojets. These parameters have previously been determined by a visual-manual method: analyzing maps of field-aligned and equivalent currents on the monitor screen and carrying out predetermined boundaries with the mouse — this took a very long time (weeks and months). The comparison between manually and automatically obtained boundaries has shown that the correlation coefficient between the two boundaries is, on average, 0.85, and the root-mean-square deviation does not exceed 2° in latitude. By providing an adequate accuracy for the boundary determination, the automatic method reduces the time for map processing by a factor of 2–3 (to minutes and hours), releasing a researcher from laborious visual work. The new method is implemented as one of the important modules in the updated MIT software.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".