{
  "url": "https://appliedfrontier.nl/en/research/signal-489d5fcf8020ccbf/",
  "title": "Forecasting space weather risks on power grids",
  "published": "2026-09-30",
  "excerpt": "Extreme space-weather events can damage power systems on Earth and degrade GPS accuracy and satellite operations. A new machine learning system can predict where damage",
  "content_sha256": "be7ba766e90ec53e2f241d1eb87cf246ea0eafef6580d4b64c3b4a18b91fcb50",
  "extracted_chars": 320,
  "artifact_links": [],
  "extraction_scope": "official_feed_metadata",
  "full_text_available": false,
  "feed_provider": "Microsoft Research",
  "id": "signal-489d5fcf8020ccbf",
  "org": "Microsoft Research",
  "topic": "AI models",
  "source_kind": "primary_technical_publication",
  "status": "unreviewed-source-report",
  "analysis_method": "deterministic_research_triage_v2",
  "novelty_verified": false,
  "proof_checked": false,
  "peer_review_verified": false,
  "matched_terms": [
    "AI models"
  ],
  "checked_at": "2026-11-01T13:17:06+00:00",
  "first_seen": "2026-10-31T19:32:56+00:00",
  "source_hash": "c2f675cca66e36fd26478674833fc3fd2ae3fee411ee6b853374e14f89dc0388",
  "source_url": "https://www.microsoft.com/en-us/research/blog/forecasting-space-weather-risks-on-power-grids",
  "language": "en",
  "indexing": "noindex_until_editorially_enriched"
}
