Total Electron Content and Ionospheric Scintillation Measurements during the Total Solar Eclipse of July 2, 2019
Bibliographic record
Abstract
Global Navigation Satellite Systems (GNSS) provide a reliable source of radio wave signals that is available at all times throughout the entire planet. These signals are known to also interact with the ionosphere, where there is a high concentration of free electrons and ions. This in turn creates a framework for scientists to continuously monitor and analyze how these signals are affected by free electron and ion concentration irregularities in this region. Such irregularities may induce fluctuations in both signal amplitude and phase known as ionospheric scintillations. The behavior of the ionosphere is also known to be directly related with solar activity as well as localized phenomena, such as solar eclipses. This study aims to measure the impact of the solar eclipse of July 2, 2019 on local ionospheric properties in terms of total electron content (TEC) and scintillation indices S4 and SigmaPhi. Two GNSS receivers (NovAtel GPStation-6) were stationed in La Serena, Chile in collaboration with the University of La Serena and in Cerro Pachón, Chile along the Andes Lidar Observatory, where they collected TEC and scintillation data prior, during and after totality. We have observed a pronounced drop and recovery of TEC on both stations as well as supporting high rate data to explore possibilities of eclipse induced scintillations.
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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 | 0.001 |
| 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".