{
  "annotation_id": "3c51c17b-1dae-4880-ba79-edb1091e4ba5",
  "date_created": "2026-03-02T18:01:03.865000Z",
  "date_modified": "2026-03-02T18:01:03.865000Z",
  "file_hash": "5447a13ff6fd740af7fbed3e8d4593bcce49ba0d7b65a5719bd7fae93515c11d",
  "private": false,
  "record": {
    "abstract": "The spatial structure of fluctuations in spatially inhomogeneous processes\ncan be modeled in terms of Gibbs random fields. A local low energy estimator\n(LLEE) is proposed for the interpolation (prediction) of such processes at\npoints where observations are not available. The LLEE approximates the spatial\ndependence of the data and the unknown values at the estimation points by\nlow-lying excitations of a suitable energy functional. It is shown that the\nLLEE is a linear, unbiased, non-exact estimator. In addition, an expression for\nthe uncertainty (standard deviation) of the estimate is derived.",
    "arxiv_id": "physics/0510035",
    "authors": [
      "D. T. Hristopulos"
    ],
    "categories": [
      "physics.data-an",
      "math.ST",
      "stat.TH"
    ],
    "doi": "10.1016/j.physa.2006.01.037",
    "journal_ref": "Physica A: Statistical Mechanics and its Applications, 365(1-2),\n  211-216 (2006)",
    "title": "Spatial Random Field Models Inspired from Statistical Physics with   Applications in the Geosciences",
    "url": "https://arxiv.org/abs/physics/0510035"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "132d5d49-fa69-41c4-bded-09b3937bc0cb",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}