Correlation between aeromagnetic data rejection and geomagnetic indices
Bibliographic record
Abstract
Abstract Predicting the rejection of aeromagnetic data would be a useful tool for aeromagnetic survey planning. To relate aeromagnetic survey requirements to geomagnetic activity monitoring and prediction, we analyzed the relationship between the rejection of aeromagnetic data as it is measured during surveys and the variations in existing geomagnetic indices. The magnetic data were collected at Canadian magnetic observatories during 2001 and covered the polar cap, auroral, and subauroral zones. The geomagnetic indices were global and local indices. The global indices included the Kp, ap, and Dst indices. The local indices were the three-component hourly ranges, the three-component maximum rate of change, and the Pc3 pulsation index. The goodness of fit was used to compare the results between the different indices at different locations. In general, there was some correlation between global geomagnetic indices and the rate of rejection of aeromagnetic data. Good correlation with a global index was obtained with the daily mean of the Ap index for a station located in the subauroral zone. The best correlation was obtained with local indices and particularly with the Pc3 index amplitude. From these results we conclude that forecasting Pc3 index amplitude would be a useful tool for planning aeromagnetic surveys.
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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 | 0.030 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".