{"id":"W4404308404","doi":"10.48550/arxiv.2410.17976","title":"metasnf: Meta Clustering with Similarity Network Fusion in R","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Campbell Family Mental Health Research Institute; University of Toronto; Hospital for Sick Children; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada; Centre for Addiction and Mental Health Foundation; Canada Research Chairs","keywords":"Similarity (geometry); Cluster analysis; Artificial intelligence; Fusion; Computer science; Pattern recognition (psychology); Philosophy; Linguistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008165547,0.004678395,0.003693995,0.005054195,0.001457051,0.004820064,0.005654874,0.001854203,0.07148521],"category_scores_gemma":[0.0351695,0.002685134,0.005292634,0.003775096,0.001434089,0.004136772,0.004807608,0.004858588,0.05479643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001612068,"about_ca_system_score_gemma":0.003670882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005753535,"about_ca_topic_score_gemma":0.007255606,"domain_scores_codex":[0.9949941,0.001771417,0.0004135135,0.001138422,0.001396254,0.000286298],"domain_scores_gemma":[0.9903595,0.005718487,0.00078258,0.001791958,0.001060592,0.0002867936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009471124,0.0001419483,0.006498569,0.004036512,0.002780237,0.0006774229,0.0009651085,0.03846535,0.006634261,0.05140752,0.7411281,0.1463179],"study_design_scores_gemma":[0.0009552491,0.000207746,0.004275552,0.0007871901,0.000872733,0.001045784,0.0003175415,0.280711,0.01934619,0.1952281,0.4956534,0.0005996029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001789633,0.0005286302,0.6101317,0.000497121,0.0003455596,0.0002245809,0.02967576,0.3537969,0.003010147],"genre_scores_gemma":[0.0409103,0.0008577651,0.7381474,0.0007813024,0.0001818453,0.002493231,0.05013357,0.1612928,0.00520183],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07148521,"threshold_uncertainty_score":0.2391418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1207095706410267,"score_gpt":0.2295672598023473,"score_spread":0.1088576891613206,"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."}}