{"id":"W4293056555","doi":"10.1101/2022.08.22.504699","title":"From components to communities: bringing network science to clustering for genomic epidemiology","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research","keywords":"Cluster analysis; Pairwise comparison; Connected component; Computer science; Giant component; Graph; Context (archaeology); Clustering coefficient; Data mining; Markov chain; Node (physics); Theoretical computer science; Biology; Artificial intelligence; Random graph; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004310157,0.001088679,0.001163707,0.005948886,0.001332351,0.003725698,0.001624798,0.001735704,0.002543174],"category_scores_gemma":[0.0238887,0.0008540208,0.001474175,0.004363772,0.003123837,0.00577493,0.004361699,0.003757746,0.0008828982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002219052,"about_ca_system_score_gemma":0.001716357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004541425,"about_ca_topic_score_gemma":0.004186605,"domain_scores_codex":[0.9969518,0.00174646,0.0001199708,0.0005149912,0.0005714819,0.00009526179],"domain_scores_gemma":[0.9869959,0.008657693,0.000798334,0.001526034,0.00142954,0.0005924811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006887395,0.00009016269,0.003476596,0.0005364656,0.0002796136,0.0002386798,0.001375058,0.1023423,0.00210032,0.7476805,0.01101517,0.1307963],"study_design_scores_gemma":[0.00001304358,0.0000140583,0.0006538375,0.000101668,0.00002597365,0.00008498065,0.0001805169,0.2158229,0.0004822008,0.7668567,0.01573135,0.00003275439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003797451,0.001427902,0.9903893,0.002035796,0.0001383891,0.00008175313,0.0001799498,0.0003304122,0.001618997],"genre_scores_gemma":[0.09624339,0.003794961,0.8947232,0.0007966628,0.0007201286,0.0003886153,0.0005494225,0.0003933698,0.002390313],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005948886,"threshold_uncertainty_score":0.0227946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04092285689588584,"score_gpt":0.2891387835323435,"score_spread":0.2482159266364576,"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."}}