{"id":"W4407531641","doi":"10.1103/physreve.111.024307","title":"Leveraging spurious Omori-Utsu relation in the nearest-neighbor declustering method","year":2025,"lang":"en","type":"article","venue":"Physical review. E","topic":"earthquake and tectonic studies","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Agencia Estatal de Investigación; European Regional Development Fund; Natural Sciences and Engineering Research Council of Canada","keywords":"Spurious relationship; Relation (database); k-nearest neighbors algorithm; Computer science; Data mining; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005404861,0.0001098093,0.0002412521,0.00003719199,0.0001376808,0.00003424092,0.0001967091,0.00001672208,0.00009235155],"category_scores_gemma":[0.0002363087,0.00006841747,0.00008783449,0.000461406,0.00002769909,0.00009649671,0.00002131892,0.0002066788,0.000136027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004849416,"about_ca_system_score_gemma":0.000026775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005114476,"about_ca_topic_score_gemma":0.0004350811,"domain_scores_codex":[0.998994,0.0002212866,0.0001885358,0.0002045046,0.0001841494,0.0002075469],"domain_scores_gemma":[0.9989256,0.0007857275,0.00004635619,0.0002013499,0.00001710151,0.00002383629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0000139204,0.00004278867,0.1647337,0.0005052802,0.0000388888,0.00002479521,0.001182263,0.0008062689,0.00002454703,0.002744804,0.0014302,0.8284525],"study_design_scores_gemma":[0.0001769764,0.00003831492,0.9449362,0.0007527976,0.00005470861,0.000004928128,0.00008396873,0.009991753,0.00003744943,0.01399346,0.02978846,0.0001409692],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7363282,0.1028391,0.003779601,0.01216794,0.0006007244,0.001219139,0.00001011084,0.0001115569,0.1429436],"genre_scores_gemma":[0.9934518,0.003751429,0.0005545428,0.00205356,0.0001007671,0.000006343438,0.000008181169,0.00000188677,0.00007145706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8283116,"threshold_uncertainty_score":0.2789983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02613494488450125,"score_gpt":0.3313213195033652,"score_spread":0.305186374618864,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}