Long‐Term Peak Geoelectric Field Behavior for Space Weather Hazard Assessment in Alberta, Canada Using Geomagnetic and Magnetotelluric Measurements
Bibliographic record
Abstract
Abstract To better understand the risks of space weather to electric power transmission networks, magnetometer data and nearby magnetotelluric impedance data at four sites in Alberta, Canada are used to estimate the induced geoelectric field over the last 12–32 years. Peak geoelectric fields >11 and ∼1 V/km are estimated in northern and southern Alberta, respectively. Peak magnitudes decrease from north to south partially due to magnetic latitude, but primarily due to variations in ground conductivity, highlighting the importance of including realistic geological information. Best estimates of 1‐in‐100 years return levels range from 2.0 to 9.2 V/km in southern and northern Alberta, respectively, exceeding 8 V/km NERC benchmarks in some cases. Large geoelectric fields can occur any time of day, although they are more likely during nightside events and on the dawn flank. Events that exceed 1 V/km can last >8 min which warrants further investigation since these events may cause more damaging GIC due to extended periods of transformer heating. The rate of change of the horizontal magnetic field () is not particularly well‐correlated with the geoelectric field (0.4 < R < 0.7), suggesting that may not always represent a good proxy for risk to the power network. The ground impedance partially explains these poor correlations; regions with a resistive surface layer (northern Alberta) have better correlations with than regions with a conductive surface layer (southern Alberta) because the shallow conductor filters high frequency components of the geoelectric field which are present in the time series.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".