Bibliographic record
Abstract
Abstract. Ground level enhancements (GLEs) result from transient intensity increases in secondary cosmic ray particles detected by ground-based neutron monitors. Characterizing the temporal evolution of GLEs provides insight into particle acceleration mechanisms and interplanetary transport processes. The present study investigates the moderate intensity GLE 72 event on 10 September 2017, which fortuitously occurred during a solar wind stream interface (SI) region impacting the Earth's magnetosphere. We quantify how transient solar wind structures modulate the observed GLE pulse shape by combining multi-station neutron monitor observations with Monte Carlo particle transport models. Based on this analysis, we find that the turbulent magnetic field within the SI significantly enhances pitch angle scattering rates for energetic particles. In comparison to typical impulsive events, particle mean free paths declined by approximately 35 % during the 6-hour SI crossing. The stochastic acceleration caused by interactions with the disturbed magnetic fields resulted in higher particle intensities later in the event. According to these results, even moderate interplanetary disturbances can significantly alter transport conditions and alter the intensity-time profiles for GLEs. This further corroborates previous findings that the traditional classification of GLEs solely based on temporal characteristics, which may be obscured by transient propagation effects is superfluous. The study highlights the need to integrate multi-spacecraft solar wind observations into the interpretation of GLEs in order to disentangle intrinsic acceleration mechanisms from interplanetary modulation processes.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.018 | 0.009 |
| Insufficient payload (model declined to judge) | 0.276 | 0.206 |
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".