How Does the Definition of a Geomagnetic Storm Affect the Contents of the Resulting Storm List?
Bibliographic record
Abstract
A geomagnetic storm is a significant and prolonged disturbance of the Earth’s magnetic field caused by an enhancement of the ring current. Storms are typically identified using magnetic field measurements from multiple ground-based magnetometers, and the corresponding dates and times of multiple storms can be collated to form a storm list. There is currently no quantitative definition of a geomagnetic storm, so the contents of a particular storm list are highly dependent on the identification criteria that were used to generate it. The different definitions and identification methods can cause the physical properties of storms in different lists to vary, and these variations may be significant. We take several storm lists with different identification methods, and we compare the probability distributions of a range of solar wind variables and geomagnetic indices between those lists. We also compare the temporal behaviour of the storm lists, and discuss how the differences may affect the way geomagnetic storms are defined and studied. We find that changing the definition of the quiet time has a direct impact on the start and end times of the storms, and this can result in a storm being significantly shorter when defined using a more negative quiet time definition. We also find that changes to the threshold value of minimum magnetic disturbance have a greater impact on the properties of the identified storms than changes to the quiet time definition. We provide recommendations on which storm lists to use for different scenarios.
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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 | 0.027 | 0.201 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".