{"id":"W2783873704","doi":"10.1186/s12859-017-1996-y","title":"diceR: an R package for class discovery using an ensemble driven approach","year":2018,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"BC Cancer Foundation","keywords":"Computer science; Dicer; Cluster analysis; Context (archaeology); Data mining; Set (abstract data type); Class (philosophy); Permutation (music); Generalization; Cluster (spacecraft); Machine learning; Artificial intelligence; Data science; Biology; Mathematics","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":[0.0004269402,0.0002005488,0.0002134319,0.0001569222,0.0003710826,0.0006900774,0.001321786,0.0001084937,0.00000137835],"category_scores_gemma":[0.0000757523,0.0001784927,0.00006904593,0.0003691603,0.0001726537,0.005290051,0.0005214868,0.0001450506,0.00002633266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001196499,"about_ca_system_score_gemma":0.0002002299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001549824,"about_ca_topic_score_gemma":0.00002727495,"domain_scores_codex":[0.9981972,0.000053307,0.0003840437,0.0003080463,0.0004424533,0.0006149227],"domain_scores_gemma":[0.9981145,0.000092479,0.0001591216,0.001200646,0.000203638,0.000229651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004607723,0.003197095,0.001674655,0.004161801,0.0002703121,0.00002035516,0.06685312,0.1652574,0.01470022,0.1356119,0.001893724,0.6058986],"study_design_scores_gemma":[0.0004738097,0.0004285909,0.00007738776,0.00001871423,0.000005614143,0.0000286026,0.0005543471,0.9946892,0.001674172,0.001102141,0.0006831681,0.0002642801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00971716,0.000007251685,0.9875722,0.00001577666,0.0002389072,0.0004676436,0.00002297247,0.0001967637,0.00176136],"genre_scores_gemma":[0.04167516,0.000002579183,0.9576384,0.0001053491,0.0002821751,0.0000255351,0.00003068943,0.00002479652,0.0002152412],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8294318,"threshold_uncertainty_score":0.7278723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08579926171176273,"score_gpt":0.3410082939630839,"score_spread":0.2552090322513211,"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."}}