The future evolution of the auroral zones
Bibliographic record
Abstract
The auroral zones indicate the locations on the Earth’s surface where, on average, it is most likely to spot aurorae as a consequence of increased solar activity. The shape of the auroral zones and, similarly, the geographical locations most vulnerable to extreme space weather events are modulated by the geomagnetic field of internal origin. As the latter evolves in time, the formers will be subject to variations on the same timescales. From available geomagnetic field forecasts (which provide an estimate of the future evolution of the geomagnetic field of internal origin) we derive AACGM latitudes and estimate the future evolution of the auroral zones. The novel aspect of this technique is that we make use of all available Gauss coefficients to produce the forecasts, while the majority of present techniques estimate the location of the auroral zones based on the dipolar coefficients only. Our results show that, while the shift of the geomagnetic dipole axis has a first order contribution, higher order Gauss coefficients contribute significantly to the location and shape of the auroral zones. The same technique is then extended to estimate the future location of the geographical location that would be, on average, most exposed to extreme space weather event. We find that the space-weather related risk will not change significantly for the UK over the next 50 years. For the Canadian provinces of Quebec and Ontario, however, we predict a significant increase in the risk associated to extreme solar activity.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".