Statistical analysis of ground based magnetic field measurements with the field line resonance detector
Bibliographic record
Abstract
Abstract. In this paper we introduce the field line resonance detector (FLRD), a wave telescope technique which has been specially adapted to estimate the spectral energy density of field line resonance (FLR) phase structures in a superposed wave field. The field line resonance detector is able to detect and correctly characterize several superposed FLR structures of a wave field and therefore constitutes a new and powerful tool in ULF pulsation studies. In our work we derive the technique from the classical wave telescope beamformer and present a statistical analysis of one year of ground based magnetometer data from the Canadian magnetometer network CANOPUS, now known as CARISMA. The statistical analysis shows that the FLRD is capable of detecting and characterizing superposed or hidden FLR structures in most of the detected ULF pulsation events; the one year statistical database is therefore extraordinarily comprehensive. The results of this analysis confirm the results of previous FLR characterizations and furthermore allow a detailed generalized dispersion analysis of FLRs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".