{"record":{"author":{"account_ref":null,"orcid":null},"builds_on":[],"content_schema":"pubphys.content.revision/1","content_sha256":"11603478b479e59a9bff344e36f98a7a23eef33522b5e9d6915affc4e59105ff","created":"2026-10-03T07:17:57Z","files":[],"origin":{"assisted_by":[],"kind":"seed"},"parents":["5475cf5dc5b1a72283d26dc95dc6e00d7ea4e00096a7c6492e409a1816eeb011"],"salt":"33024ea93d297abcd35048eb80d4d31653f538ae9a5a0669e31b6d0187b766db","schema":"pubphys.record/2","site":"pubphys.com","target":null,"type":"revision"},"content":{"answer_type":"classification","assisted_by":[],"external_id":"bio.fitness-landscapes.beneficial-dfe","kind":"well-posed","literature_status":"contested","n":"1","parents":[],"plain":"Extreme-value statistics, the mathematics of the largest values in a random sample, predicts that rare improving mutations in a well-adapted organism follow a simple exponential law. Experiments give mixed support, and which statistical class real mutations follow is unsettled.","posed_since":"","precise":"If the wild type is near the top of the fitness distribution of its mutational neighbors, extreme-value theory predicts exponentially distributed beneficial selection coefficients s (Gumbel domain), with Weibull (bounded) or Frechet (heavy-tailed) alternatives. Determine the domain of attraction from large mutant libraries (deep mutational scanning, barcoded lineage tracking) across organisms and environments, and how it depends on distance from the optimum.","problem_ref":null,"references":"","settled_by":"Large-library measurements of beneficial effects with tail-shape estimation across several organisms and environments.","status_note":"","title":"Distribution of fitness effects of beneficial mutations","topic_ref":"d9679f27d8e24851339481c38577825fa8afb7298403d565282e3b7505aed4ee"},"attested":{"attestation":{"batch":null,"client_id":null,"id_token_sha256":null,"kind":"platform"},"record_hash":"cd7159bde0db23dbae967ebe2932c116271f0dc2c0f2a577985f21fc49015005","schema":"pubphys.attested/1"},"envelope":{"attested_hash":"b66c1fb0d9b07f2be3c18e6ef6a2e17b4233cf132eff04dee5904bafecd5159e","platform_signature":{"key_id":"c6afc19b31429869751f06879c75cd64ea92654423d15b44be775bf1310a60da","sig":"a0JsT_u7URyF-9WshF9-MCwz_guIrX_5G4bCH2CVQKQI5X2N6NOYexGGcbdVJObhVUoeeE3-DKzM7uZ6qXXGBQ"},"schema":"pubphys.envelope/1"},"record_hash":"cd7159bde0db23dbae967ebe2932c116271f0dc2c0f2a577985f21fc49015005","leaf_index":766}