On Optimum Solar Wind‐Magnetosphere Coupling Functions for Transpolar Voltage and Planetary Geomagnetic Activity
Bibliographic record
Abstract
Abstract Using 65,133 hourly averages of transpolar voltage ( Φ PC ) from observations made over 25 yr by the SuperDARN radars, with simultaneous SML and interpolated am geomagnetic indices, we study their optimum interplanetary coupling functions. We find mean lags of 18, 31, and 45 min for Φ PC , am , and SML , respectively, and fit using a general coupling function with three free fit exponents. To converge to a fit, we need to average interplanetary parameters and then apply the exponent which is a widely used approximation: we show how and why this is valid for all interplanetary parameters, except the factor quantifying the effect of the clock angle of the interplanetary magnetic field, sin d ( θ /2), which must be computed at high time resolution and then averaged. We demonstrate the effect of the exponent d on the distribution, and hence weighting, of samples and show d is best determined from the requirement that the coupling function is a linear predictor, yielding d of 2.50 ± 0.10, 3.00 ± 0.22, and 5.20 ± 0.41 for Φ PC , am , and SML , respectively. To check for overfitting, fits are made to half the available data and tested against the other half. Ensembles of 1,000 fits are used to study the effect of the number of samples on the distribution of errors in individual fits and on systematic biases in the ensemble means. We find only a weak dependence of solar wind density for Φ PC and SML but a significant one for am . The optimum coupling functions are shown to be significantly different for Φ PC , am , and SML .
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".