Ionospheric Sounding and Tomography Using Automatic Identification System (AIS) and Other Signals of Opportunity
Bibliographic record
Abstract
Abstract Numerical modeling has demonstrated that Automatic Identification System (AIS) signals can be used not only to estimate vertical total electron content (TEC) to supplement current TEC maps and data assimilation models but also to reconstruct two‐dimensional (2‐D) electron density maps of the ionosphere using computerized tomography. A ray tracing model was used to determine the characteristics of individual linearly polarized waves transmitted by ships to satellites in circular orbits at 780‐ and 1,000‐km altitude, including the wave path and the state of polarization at the satellite receiver. The modeled Faraday rotation was computed and used to calculate the TEC along the ray paths. The resulting TEC was used as input for computerized ionospheric tomography using the algebraic reconstruction technique. This study concentrated on reconstructing mesoscale structures 25–100 km in horizontal extent. The primary scientific interest of this study was to show that AIS signals can be used as a new source of input data for computerized ionospheric tomography to image the ionosphere and to obtain a better understanding of magneto‐ionic wave propagation.
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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) | 0.001 | 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".