{"id":"W3011819927","doi":"10.1142/s0218488520500087","title":"Estimating and Controlling Overlap in Gaussian Mixtures for Clustering Methods Evaluation","year":2020,"lang":"en","type":"article","venue":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thales (Canada)","funders":"","keywords":"Cluster analysis; Computer science; Data mining; Mixture model; Set (abstract data type); Artificial intelligence; Data set; Machine learning; Pattern recognition (psychology)","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":[0.06062366,0.002529714,0.002219703,0.007013642,0.002207185,0.00517155,0.002547976,0.002971475,0.001435389],"category_scores_gemma":[0.2361425,0.001007828,0.001681823,0.003172832,0.004920748,0.007144297,0.006533527,0.003019268,0.0003187158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003985068,"about_ca_system_score_gemma":0.003172735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003685126,"about_ca_topic_score_gemma":0.002500806,"domain_scores_codex":[0.952856,0.02483638,0.003254769,0.003219112,0.01464185,0.001191855],"domain_scores_gemma":[0.8210326,0.1423483,0.009332345,0.01209786,0.01393837,0.001250576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005999128,0.0003237817,0.01294532,0.0005893187,0.0004156394,0.0001948574,0.001433807,0.6141857,0.01421602,0.1741812,0.001144278,0.1797703],"study_design_scores_gemma":[0.00002348451,0.000232764,0.002485324,0.0001303061,0.00005594429,0.0001207725,0.0002132193,0.9297736,0.01621731,0.04922977,0.001428238,0.00008929757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01377921,0.0002548092,0.9848019,0.00008610837,0.00002079689,0.000109208,0.00003908531,0.0002610676,0.0006478113],"genre_scores_gemma":[0.3085519,0.0001959781,0.6898597,0.00006827438,0.00004081409,0.0004520684,0.0002051527,0.0002670277,0.0003590498],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06062366,"threshold_uncertainty_score":0.3206124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05904577131457585,"score_gpt":0.4037623164225094,"score_spread":0.3447165451079335,"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."}}