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    "headline": "The Forecast Began by Choosing the Planet",
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    "dek": "An autonomous system assembled public and Earth-observation data on demand, then searched for a task-specific model instead of beginning with a fixed dataset.",
    "summary": "An autonomous system assembled public and Earth-observation data on demand, then searched for a task-specific model instead of beginning with a fixed dataset.",
    "body_text": "PPE retrieves spatiotemporally relevant covariates from public and Earth-observation platforms, fuses them with foundation-model embeddings, and searches model families with overfitting guards. The authors report mean R-squared gains across 21 US health indicators, national risk and vulnerability measures, a doubling over a baseline for Nigerian food-security downscaling, and 83.3 percent Recall@10 in five retrospective weekly forecasts for the 2026 DRC Bundibugyo Ebola outbreak. Those are author-reported evaluations on selected tasks; an autonomous pipeline does not remove the need to audit data coverage, target validity, uncertainty, or decisions made from its forecasts.",
    "why_it_matters": "An autonomous system assembled public and Earth-observation data on demand, then searched for a task-specific model instead of beginning with a fixed dataset.",
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      "Those are author-reported evaluations on selected tasks; an autonomous pipeline does not remove the need to audit data coverage, target validity, uncertainty, or decisions made from its forecasts."
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    "tags": [
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      "automated modeling"
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      "title": "arXiv preprint 2608.26088",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2608.26088",
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      "published_at": "2026-08-26T13:50:52.000-04:00",
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    "name": "The Machine Press",
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    "description": "A daily newspaper for the age of artificial intelligence."
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    "title": "The Forecast Began by Choosing the Planet",
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