{"id":"W2100405508","doi":"10.1109/nafips.2008.4531218","title":"Discovering structure in labeled data","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Computer science; Hierarchical clustering; Class (philosophy); Similarity (geometry); Data mining; Measure (data warehouse); Cluster (spacecraft); Artificial intelligence; Similarity measure; Data structure; Pattern recognition (psychology); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004639487,0.0008232928,0.001440204,0.004642426,0.00176616,0.00283087,0.002216145,0.001929385,0.0009637668],"category_scores_gemma":[0.02193681,0.0008165502,0.001192744,0.004140037,0.00185606,0.004341541,0.002735205,0.002113548,0.0006815513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001447445,"about_ca_system_score_gemma":0.002411943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002527692,"about_ca_topic_score_gemma":0.004873801,"domain_scores_codex":[0.9935917,0.002375156,0.0003536516,0.001477796,0.001938098,0.0002634389],"domain_scores_gemma":[0.9815042,0.009555252,0.002120035,0.003961768,0.002543856,0.0003149418],"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":[0.0004036914,0.0004515033,0.01981875,0.0008440035,0.000416562,0.0007919188,0.002116667,0.1797389,0.01092538,0.1588833,0.01323945,0.6123699],"study_design_scores_gemma":[0.00003716747,0.00007862285,0.001370268,0.0001052165,0.00006742918,0.000202705,0.0003188452,0.7715454,0.005420796,0.2129793,0.007827743,0.00004662267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01138288,0.0002618049,0.9863521,0.0003299396,0.00004581139,0.00009498417,0.0003459577,0.0004098748,0.0007766549],"genre_scores_gemma":[0.1501999,0.0004092749,0.8453082,0.0002696394,0.0001641979,0.0002719857,0.002323846,0.00009387991,0.0009590607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004642426,"threshold_uncertainty_score":0.02453625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08347915006166517,"score_gpt":0.329163074094694,"score_spread":0.2456839240330288,"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."}}