Determination of the Refractive Contribution to GPS Phase “Scintillation”
Bibliographic record
Abstract
Abstract As L‐band radio waves travel through the ionosphere, such as those transmitted by the Global Positioning System (GPS) satellites, changes in the electron density along the ray path may induce refractive and/or diffractive variations in the signal's phase; where refractive variations are deterministic and diffractive variations are stochastic. Typically, the refractive component of these variations is thought to be slow varying, associated with frequencies less than 0.1 Hz. Therefore, if the refractive contribution is assumed to be associated with frequencies less than 0.1 Hz, the frequencies greater than 0.1 Hz are then assumed and treated as diffractive. These variations are usually referred to as scintillation. In scintillation studies the deterministic refractive variations are very often ignored. We propose that rapid changes in the electron density, and therefore changes in the refractive index along the ray path of the GPS signal, can induce dominantly refractive variations at frequencies greater than 0.1 Hz. The increased drift speeds observed in the high‐latitude region create conditions suitable for these high‐frequency refractive variations; the GPS ray path will sweep through large‐scale irregularities at higher speeds, resulting in high‐frequency refractive variations. Using recent advances in GPS, most importantly an improved signal tracking technique, we present examples of rapid refractive variations in the GPS signal's phase. These high‐frequency variations are shown to be refractive using a combination of techniques, one adapted from a previous technique used for the low‐frequency refractive contributions and a new technique only possible with advances in GPS tracking.
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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".