Rishbeth PRize • tuRnbull: GeomaGnetically induced currents
Bibliographic record
Abstract
Although the UK is not normally consid-ered to be at risk from geomagnetically induced currents (GIC, see page 5.23), recent severe geomagnetic storms driven by solar activity have resulted in large GIC being observed in Scotland. Although not large enough to cause blackouts such as those in Sweden and Canada, they are indicative of the potential problems during large geo-magnetic storms, which can occur with little warning. As society becomes ever more dependent on technology, the space-weather hazard should be assessed for mid-latitude countries such as the UK. As this is a relatively small country, its power network (the National Grid) does not contain the long power lines present in countries such as Canada, which can produce large GIC. However, the UK grid is complex and with the future possible inclusion of, for example, isolated wind farms, may soon include longer lines. The UK also has a complicated geological structure and is surrounded by a shallow sea shelf, with the deep ocean on one side and continental Europe on the other – factors that play a role in the production of GIC during geomagnetic storms. In addition, the National Grid is not isolated; it is con-nected to Northern Ireland and France by undersea direct current power transmission lines. In the long term this means that the effect of GIC in Europe needs to be considered. For the time being, we have studied the UK in isolation. A GIC model that already exists at the British Geological Survey (Thompson 2005) has been used to evaluate the risk of GIC in the Scottish section of the grid, by comparing predicted GIC results to GIC data, which are monitored by Scottish Power at four locations: Hunterston, Neilston, Strathaven and Torness. These same sites are used to test a new model of the entire
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.147 | 0.063 |
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".