Statistical study of ELF/VLF emissions at subauroral latitudes in Athabasca, Canada
Bibliographic record
Abstract
Abstract We present the first statistical analysis of ELF/VLF emissions observed on the ground at subauroral latitudes that includes their features, occurrences, and association with solar wind and geomagnetic variations. Using a 100 kHz sampling loop antenna located in Athabasca, Canada (54.60°N, 246.36°E, L = 4.3), we monitored these emissions, including chorus, quasiperiodic emissions, and hiss, from November 2012 to October 2013. We found a maximum occurrence rate in the morning sector (06–07 MLT, magnetic local time) and a minimum in the night sector (∼18 to 02 MLT), in agreement with previous satellite measurements in the inner magnetosphere. We also found correlation between the ongoing substorm and storm activity and the increase of occurrence rates. The observed waves usually had a central frequency ∼1–3 kHz lower than the half‐gyrofrequency at the conjugate equatorial plane, indicating a wave source at higher latitudes. A superposed epoch analysis showed that the starting time of the ELF/VLF emissions is preceded by a rise in AE both on short (hours) and long (days) terms. Solar wind speed also started slowly rising ∼1.5 days before, while density and dynamic pressure decreased shortly afterward. This may signify that high‐speed solar wind conditions also contribute to the generation of ELF/VLF emissions detected at subauroral latitudes.
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".