Bibliographic record
Abstract
Magnetic Anomaly Detection (MAD) is an application in which airborne magnetometers are used to detect small magnetic variations against the Earth's background magnetic field. This technique is used in aeromagnetic surveys, to detect mineral deposits and in applications such as antisubmarine warfare. The magnetic signals of interest typically have periods of 1–100 s and amplitudes of 0.001–1 nT. In order to isolate and detect such signals, all other sources of magnetic noise in this frequency band must be modeled, or measured, and mitigated. Despite reduction of many error sources for MAD, a limiting factor remains: the small‐amplitude variations caused by geomagnetic pulsations. In the frequency band of interest for MAD (0.01–1 Hz), Pc 3 pulsations represent a significant error source. These continuous pulsations are apparent as pulse trains in magnetic time series for intervals as long as several minutes. These pulsations arise from resonant oscillations in the dayside magnetosphere driven by the solar wind. Such fluctuations may be observed in GPS total electron content (TEC) observations. In this paper, analyses are conducted using 1 Hz data available from GPS reference stations and colocated magnetometers in Canada and Australia. Relative TEC variations are derived from the precise dual‐frequency GPS carrier phase observations and band‐pass‐filtered. Dominant TEC variations at Pc 3 frequencies are then correlated with local magnetic time series from the ground reference. Results are analyzed as a function of solar wind parameters, and the potential for exploiting standalone GPS to derive Pc 3 pulsation indices is investigated.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".