GPS TEC, scintillation and cycle slips observed at high latitudes during solar minimum
Bibliographic record
Abstract
Abstract. High-latitude irregularities can impair the operation of GPS-based devices by causing fluctuations of GPS signal amplitude and phase, also known as scintillation. Severe scintillation events lead to losses of phase lock, which result in cycle slips. We have used data from the Canadian High Arctic Ionospheric Network (CHAIN) to measure amplitude and phase scintillation from L1 GPS signals and total electron content (TEC) from L1 and L2 GPS signals to study the relative role that various high-latitude irregularity generation mechanisms have in producing scintillation. In the first year of operation during the current solar minimum the amplitude scintillation has remained very low but events of strong phase scintillation have been observed. We have found, as expected, that auroral arc and substorm intensifications as well as cusp region dynamics are strong sources of phase scintillation and potential cycle slips. In addition, we have found clear seasonal and universal time dependencies of TEC and phase scintillation over the polar cap region. A comparison with radio instruments from the Canadian GeoSpace Monitoring (CGSM) network strongly suggests that the polar cap scintillation and TEC variations are associated with polar cap patches which we therefore infer to be main contributors to scintillation-causing irregularities in the polar cap.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".