{"id":"W4407937849","doi":"10.1109/tpami.2025.3545573","title":"Gauging-: A Non-Parametric Hierarchical Clustering Algorithm","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cluster analysis; Artificial intelligence; Hierarchical clustering; Algorithm; Pattern recognition (psychology); Parametric statistics; Data mining; Mathematics; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003408902,0.0002792597,0.0004166602,0.002030896,0.0003353189,0.0002744802,0.0008665915,0.00008980843,0.0000640205],"category_scores_gemma":[0.00001326838,0.0002587305,0.0002652416,0.004581686,0.0001096167,0.0003503725,0.00004067245,0.0006212305,0.00003041186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009529044,"about_ca_system_score_gemma":0.00004572442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000836284,"about_ca_topic_score_gemma":0.0002230248,"domain_scores_codex":[0.9977448,0.0001034491,0.0004427622,0.0008304475,0.0004195661,0.0004589539],"domain_scores_gemma":[0.9985018,0.0003570542,0.00006953127,0.0007804362,0.0001033185,0.0001878255],"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":[0.000006484982,0.0001073868,0.0000615908,0.00002136896,0.0003318501,0.00002111217,0.0001043641,0.06060304,0.00008403469,0.0000265304,0.000002414984,0.9386298],"study_design_scores_gemma":[0.0001200469,0.00009398987,0.0005679784,0.0000390776,0.0001284371,0.00001281467,0.00001996166,0.9695007,0.02884219,0.0003784318,0.00006609859,0.0002303316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000360235,0.0001054132,0.9981304,0.0006080218,0.0002791956,0.0001720412,0.00001596364,0.000135246,0.0001934615],"genre_scores_gemma":[0.9320175,0.0003078257,0.06626255,0.0005129637,0.00001627513,0.00004992538,0.000002215084,0.00001245241,0.0008182681],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9383995,"threshold_uncertainty_score":0.9999865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01791034889141219,"score_gpt":0.3089522056968399,"score_spread":0.2910418568054277,"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."}}