Understanding space weather hazard in the Australian high-voltage power transmission lines using geomagnetic and magnetotelluric data
Bibliographic record
Abstract
Geoscience Australia's geomagnetic observatory network covers one-eighth of the Earth. The first Australian geomagnetic observatory was established in Hobart in 1840. This almost continuous 180-year period of magnetic-field monitoring provides an invaluable dataset for scientific research. Geomagnetic storms induce electric currents in the Earth that feed into power lines through substation neutral earthing, causing instabilities and sometimes blackouts in electricity transmission systems. Power outages to business, financial and industrial centres cause major disruption and potentially billions of dollars of economic losses. The intensity of geomagnetically induced currents is closely associated with geological structure. We modelled peak geoelectric field values induced by the 1989 Quebec storm for south-eastern Australian states using a scenario analysis. Modelling shows the 3D subsurface geology had a significant impact on the magnitude of induced geoelectric fields, with more than three orders of magnitude difference across conductive basins to resistive cratonic regions in south-eastern Australia. We also estimated geomagnetically induced voltages in the Australian high-voltage power transmission lines by using the scenario analysis results. The geomagnetically induced voltages may exhibit local maxima in the transmission lines at differing times during the course of a magnetic storm depending on the line's spatial orientation and length with respect to the time-varying inducing field. Real-time forecasting of geomagnetic hazards using Geoscience Australia's geomagnetic observatory network and magnetotelluric data from the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP) helps develop national strategies and risk assessment procedures to mitigate space weather hazard.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".