Impacts of the Great May 2024 Geomagnetic Storm on Precise Point Positioning
Bibliographic record
Abstract
The adverse effects of space weather can be one of the main threats to human technology, such as the effects of large coronal mass ejections. Consequently, great geomagnetic storms can compromise the performance of Precise Point Positioning (PPP). Recently, after over two decades, on 05/10/2024, the main phase of a great and exceptional geomagnetic storm began, providing an excellent and rare opportunity to study its effects on the PPP using the CSRS-PPP online service. This work aims to evaluate the impacts of the great storm on three-dimensional accuracies, percentage of ambiguity resolution, and cycle slip, encompassing five days of processing, covering two days before the storm, one day coinciding with the main phase of the storm, and two days after the storm, coinciding with the recovery phase. The processing was carried out at an interval of 1 hour, from 23-24 UTC, coinciding with the main phase (05/10/2024), as the adverse effects of the storm manifest themselves in this phase. Data from 20 GNSS stations from the Brazilian Network for Continuous Monitoring of GNSS Systems were used. During the main phase of the storm, the average three-dimensional accuracy increased around four times compared to the previous day. The percentage of ambiguity resolution was equal to 0.0% for all stations analyzed. Furthermore, cycle slips increased significantly during the main phase of the storm. In summary, the results highlight the adverse impacts of the great and exceptional storm on the PPP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".