Measuring Power of Earth Disturbances Using Radio Wave Phase Imager
Bibliographic record
Abstract
Numerous studies have investigated ionospheric waves, also known as ionospheric disturbances. These disturbances exhibit complex wave patterns similar to those produced by solar, geomagnetic, and meteorological disturbances and human activities within the Earth’s atmosphere. The radio wave phase imager described herein measures the power of the ionospheric waves using their phase shift seen in phase images produced by the Long Wavelength Array (LWA) at the New Mexico Observatory, a high-resolution radio camera. Software-defined radio (SDR) was used for processing the data to produce an amplitude image and phase image. The phase image revealed the ionospheric waves, whereas the amplitude image could not see them. From the phase image produced from the carrier wave received at the LWA, the properties of the ionospheric waves have been previously characterized in terms of their energy and wave vector. In this study, their power was measured directly from the phase shift of the strongest set of ionospheric waves. The power of these waves, which originated at Albuquerque, the local major power consumer, was 15.3 W, producing a power density of 0.018 W/m2. The calculated power density that should be generated from the local power generating stations around Albuquerque was also 0.018 W/m2, in agreement with the experimentally measured value. This correspondence shows that the power generated by power stations and being consumed is not lost but captured by the ionosphere.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".