On the Estimation of Resonance Widths of Field Line Resonances Using Ground Magnetometer Data
Bibliographic record
Abstract
This paper presents a comparison of three methods to estimate the latitudinal resonance width of field line resonant, ultra-low frequency waves detected at the ground. These are a spatial domain, full-width half-maximum method and frequency domain amplitude-phase and amplitude-division methods. These methods were used to estimate the resonance width of several field line resonant intervals occurring on 26 November 2001, 1 October 2012, and 19 June 2015. The 19 June 2015 interval used data from one low, two mid, and one high latitudes. It was found that the resonance width estimates were different for each method and with how the data were processed. The most suitable methods and data processing were determined from a damped driven harmonic oscillator model. The amplitude-division methods yielded the most accurate results when the ground magnetic field data were processed with a boxcar window function or a frequency domain, exponential smoothing taper. The amplitude-phase method tended to underestimate the resonance width. The full-width half-maximum method gave accurate results for a high spatial resolution linear piece-wise curve fitted to the spectral amplitude with latitude profile. An accurate estimate of the latitudinal resonance width requires a correct choice of data processing, estimate method, resonance profile with latitude, and resonance model.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".