EclipseNB: A Radio Instrument Network for Monitoring the Ionosphere During the April 8, 2024 Total Solar Eclipse
Bibliographic record
Abstract
On April 8, 2024 there will be a total solar eclipse spanning Mexico, Central-East United States, and Eastern Canada. A total solar eclipse has a dramatic effect on the structure, dynamic behaviour, and coupling of the Earth's atmosphere-ionosphere-magnetosphere (AIM) system, and presents a unique opportunity to study the physics and chemistry at play during a sudden and localized reduction in solar radiation. As demonstrated in studies of previous eclipse events such as the August 2017 “Great American Eclipse”, there remains several outstanding questions pertaining to the complex physical processes that occur within the AIM environment during a solar eclipse. Addressing many of these open issues requires enhanced observational capabilities, including high spatial and temporal resolution observations of the ionosphere during a solar eclipse. To monitor the ionosphere over Eastern Canada during the April 2024 event, we are in the process of installing 49 Global Navigation Satellite System (GNSS) receivers, including 30 high-rate scintillation monitors, as well as 3 ionosonde systems. This radio remote-sensing network will be located within and adjacent to the path of totality, and is designed to observe the multi-scale (sub-km to 100s of kms) structuring of the ionosphere during the eclipse event. We will discuss the details of the EclipseNB network and the potential applications of these observations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".