{
  "annotation_id": "310b69a3-0b29-4c7e-8cff-2318f407ffe3",
  "date_created": "2026-03-02T18:01:28.608000Z",
  "date_modified": "2026-03-02T18:01:28.608000Z",
  "file_hash": "4f7a82027d79a6d6a69beb557adcfd86d52d92235f1db59e4e5c6b781bb916d1",
  "private": false,
  "record": {
    "abstract": "Recent work on mutation-selection models has revealed that, under specific\nassumptions on the fitness function and the mutation rates, asymptotic\nestimates for the leading eigenvalue of the mutation-reproduction matrix may be\nobtained through a low-dimensional maximum principle in the limit N to infinity\n(where N is the number of types). In order to extend this variational principle\nto a larger class of models, we consider here a family of reversible N by N\nmatrices and identify conditions under which the high-dimensional Rayleigh-Ritz\nvariational problem may be reduced to a low-dimensional one that yields the\nleading eigenvalue up to an error term of order 1/N. For a large class of\nmutation-selection models, this implies estimates for the mean fitness, as well\nas a concentration result for the ancestral distribution of types.",
    "arxiv_id": "q-bio/0311020",
    "authors": [
      "E. Baake",
      "M. Baake",
      "A. Bovier",
      "M. Klein"
    ],
    "categories": [
      "q-bio.PE"
    ],
    "doi": "10.1007/s00285-004-0281-7",
    "journal_ref": "J. Math. Biol. 50 (2005), 83--114",
    "title": "An asymptotic maximum principle for essentially linear evolution models",
    "url": "https://arxiv.org/abs/q-bio/0311020"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "6036aaf6-2afc-4455-8976-d8c9f05ea731",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}