GNSS Observations of the 14 October 2023 Annular Solar Eclipse and the 8 April 2024 Total Solar Eclipse
Bibliographic record
Abstract
Eclipse studies for the 2017 total solar eclipse over the USA benefited greatly from the vast increase in fidelity and coverage of ground-based monitoring tools, especially GNSS monitoring of total electron content (TEC). During the 2017 eclipse, total electron content (TEC) depletions up to 60% in magnitude were reported (Coster et al. 2017). These were greater than those predicted by models. Wave responses in the form of traveling ionospheric disturbances (TIDs) following this eclipse were also reported (Zhang et al., 2017). Two major solar eclipses within the 2023-2024 time frame have crossed over North America. The first was an annular solar eclipse that occurred on 14 October 2023. It began in the United States, traveling from the coast of Oregon at approximately 9 am local time (PDT) and crossed into Nevada, Utah, New Mexico before reaching the Texas Gulf Coast at approximately noon local time (CDT). During an annular eclipse, the Moon is further away from the Earth than during a total solar eclipse. Because of this, the Moon does not totally obscure the Sun during the actual eclipse and a thin ring of the Sun’s surface remains visible. The reduction in solar radiation is not 100%, so the effect on the ionosphere’s TEC is somewhat less than during a total eclipse. On 8 April 2024, another total solar eclipse crossed North America, passing over Mexico, the United States, and Canada. The eclipse began in the United States in Texas, and then traveled through Oklahoma, Arkansas, Missouri, Illinois, Kentucky, Indiana, Ohio, Pennsylvania, New York, Vermont, New Hampshire, and Maine before entering Canada. For both of these eclipses, we have deployed numerous GNSS receivers collecting 1-second data along the path of the eclipse. In addition, we have deployed a specialized GNSS scintillation receiver outside of Austin, TX. These GNSS TEC observations have been included in the standard Millstone Hill Geospace Facility’s GNSS TEC and differential TEC processing, utilizing 2000+ receivers in the continental U.S. We report here on initial observations following both of these eclipses, including a discussion of the TEC dynamics following the path of the eclipse, the percent of observed TEC depletions, and TID analysis. Comparisons of the size of observed TEC depletions will be made with those predicted by models.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".