{"id":"W2743551752","doi":"10.5281/zenodo.839848","title":"Dm-Phyclus: A Bayesian Phylogenetic Algorithm For Infectious Disease Transmission Cluster Inference","year":2017,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Phylogenetic tree; Inference; Bayesian probability; Bayesian inference; Cluster (spacecraft); Algorithm; Infectious disease (medical specialty); Computer science; Transmission (telecommunications); Computational biology; Artificial intelligence; Biology; Disease; Medicine; Genetics; Computer network; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002829204,0.001963344,0.001623929,0.002680114,0.001087921,0.002415797,0.004833734,0.002190435,0.07302658],"category_scores_gemma":[0.01201469,0.001154688,0.001912624,0.004233328,0.0006313334,0.001354188,0.002307901,0.002830668,0.0573697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001749065,"about_ca_system_score_gemma":0.0033243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009001642,"about_ca_topic_score_gemma":0.01792665,"domain_scores_codex":[0.9985918,0.000360116,0.0002022764,0.0004085944,0.0002784727,0.0001587972],"domain_scores_gemma":[0.9962885,0.001675296,0.0002614602,0.001097224,0.0004452188,0.0002323063],"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":[0.0003063804,0.00006612475,0.002976026,0.001205485,0.0001633365,0.00007333611,0.0000679019,0.005017001,0.0006160805,0.003242907,0.9795338,0.006731587],"study_design_scores_gemma":[0.001610735,0.00006233874,0.006337807,0.0005231691,0.0001684299,0.0002566724,0.0001480193,0.01557453,0.002754604,0.02470406,0.9477347,0.0001250511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.0004097967,0.00004167675,0.001888514,0.00008933811,0.00003416822,0.00003758167,0.9944343,0.002446588,0.0006179734],"genre_scores_gemma":[0.001513153,0.00004599136,0.004816727,0.00006800363,0.000008324098,0.0002270966,0.9920911,0.0007958225,0.0004338232],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.07302658,"threshold_uncertainty_score":0.2442982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01848566755479224,"score_gpt":0.2672485937261221,"score_spread":0.2487629261713299,"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."}}