Examining the Power Law Relationship Between Absorption and Frequency Using Spectral Riometer Data
Bibliographic record
Abstract
Abstract High frequency radio wave propagation is sensitive to absorption in the D and lower E‐region ionosphere. Absorption models typically characterize attenuation expected at 30 MHz, meaning scaling relationships are required to map to absorption expected at other frequencies. This is important when evaluating absorption at <20 MHz, as these frequencies are typically used for communication, and are highly sensitive to ionospheric disturbances. Typically, a power law relationship between absorption and frequency with a coefficient of n = −2 is used. This relationship can be demonstrated through consideration of the Appleton‐Hartree equation. This paper examines the performance of this relationship using data from the Kilpisjärvi Atmospheric Imaging Receiver Array for 13–14 November 2012. Using absorption measured at 30 MHz as a baseline, the power law relationship was used to calculated absorption at frequencies of 10–80 MHz. For this event, the power law relationship performed well when the measured absorption at 30 MHz was <1–2 dB, but strongly overestimated measurements as absorption increased. Performance improved when n was allowed to vary as a function of the overall level of absorption at 30 MHz. This accounts for local ionospheric changes associated with absorption events that change the balance of parameters in the Appleton‐Hartree equation causing deviation from n = −2. To further accommodate deviations associated with both local ionospheric disturbances and ambient electromagnetic noise contributions, an empirical relationship relating the logarithm of absorption to frequency was evaluated as a function of overall absorption. Compared to the simplified n = −2 power law relationship between absorption and frequency, the new relationship better represents measured absorption for the event studied.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".