{"id":"W2990511412","doi":"10.1109/access.2020.2968290","title":"K-MACE and Kernel K-MACE Clustering","year":2020,"lang":"en","type":"preprint","venue":"IEEE Access","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mace; Cluster analysis; Estimator; Kernel (algebra); Computer science; Mathematics; Cluster (spacecraft); Algorithm; Artificial intelligence; Statistics; Combinatorics","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":[0.0064306,0.001300963,0.001981795,0.003256255,0.001737235,0.002737414,0.003467753,0.002964634,0.002515943],"category_scores_gemma":[0.03740092,0.0007247764,0.001769442,0.00333154,0.002315753,0.004212359,0.003159681,0.002680213,0.001796461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001611525,"about_ca_system_score_gemma":0.002237319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004520086,"about_ca_topic_score_gemma":0.004292914,"domain_scores_codex":[0.9931234,0.002238557,0.0004647289,0.001463747,0.002378779,0.0003309271],"domain_scores_gemma":[0.983363,0.00538023,0.001305894,0.004413354,0.005209453,0.0003280882],"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":[0.0005466985,0.0001652333,0.003692296,0.0006641466,0.0004507277,0.0001740079,0.0006318177,0.4952365,0.00701066,0.140868,0.0114311,0.3391287],"study_design_scores_gemma":[0.00002116696,0.00005145089,0.001375853,0.00003739575,0.00003331107,0.0001868971,0.00007607489,0.9419079,0.004734457,0.04360985,0.007899689,0.0000659685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004739114,0.0006808368,0.9926773,0.0001961815,0.00008486155,0.00007048553,0.0001029203,0.000373461,0.00107474],"genre_scores_gemma":[0.175137,0.0006364487,0.8184577,0.0002871046,0.0001595437,0.0003344462,0.0008028583,0.0004216154,0.003763155],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0064306,"threshold_uncertainty_score":0.03400862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09332408950713195,"score_gpt":0.3866592203868237,"score_spread":0.2933351308796917,"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."}}