Bibliographic record
Abstract
A new empirical model for predicting ground‐level geomagnetic perturbations has been developed. This model is based on global measurements of the magnetic field at multiple stations in the Northern Hemisphere collected over an 8 year period, along with the simultaneous measurements of the interplanetary magnetic field (IMF). Variations in ionospheric conductivity are implicitly contained in the measurements used in the model's construction, including the solar F10.7 index. Provided with the IMF, solar wind velocity, dipole tilt angle (for season), and F10.7 index, this model computes all three vector components of the magnetic perturbations at specified locations. The model results are consistent with the corresponding maps of the ionospheric electric potential. Interestingly, maps of the vertical component have patterns that resemble maps of the overhead, ionospheric field‐aligned currents. Comparisons of model calculations with measurements at different locations show very good results, particularly at low frequencies. There are random variations at higher frequencies that are not reproduced well with the model, but they tend to occur in proportion to the predicted levels. This model could be useful for providing regional forecasts of geomagnetic activity with an approximately 1 h lead time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".