{"id":"W2278388756","doi":"10.7717/peerj.1720","title":"A comparison of clustering methods for biogeography with fossil datasets","year":2016,"lang":"en","type":"article","venue":"PeerJ","topic":"Statistical Methods and Applications","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Ontario Museum","funders":"","keywords":"Cluster analysis; Hierarchical clustering; Euclidean distance; Computer science; Set (abstract data type); Complete-linkage clustering; Data mining; Similarity (geometry); Cluster (spacecraft); Complete linkage; Single-linkage clustering; Data set; Range (aeronautics); Mathematics; Fuzzy clustering; Artificial intelligence; Biology; CURE data clustering algorithm","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":[0.035514,0.001462985,0.001503983,0.007634901,0.001522346,0.002793503,0.003189982,0.001816921,0.004784268],"category_scores_gemma":[0.09682145,0.0006669181,0.002336758,0.007900693,0.001038681,0.003670615,0.002452232,0.001933586,0.002329917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002120133,"about_ca_system_score_gemma":0.002587966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006800317,"about_ca_topic_score_gemma":0.007127841,"domain_scores_codex":[0.972769,0.01670169,0.001376248,0.002286074,0.00647449,0.0003926243],"domain_scores_gemma":[0.9324544,0.050496,0.001940926,0.00629316,0.008192325,0.0006231967],"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":[0.002302994,0.0003716521,0.0200475,0.004515328,0.00365149,0.0002573551,0.002579028,0.1901378,0.004812865,0.05178551,0.03436537,0.685173],"study_design_scores_gemma":[0.0003908353,0.0007402872,0.02808777,0.001491482,0.0004297561,0.0007403556,0.001533549,0.8176283,0.006583587,0.07387314,0.06803896,0.0004619923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06282783,0.007475921,0.9075168,0.001409748,0.0009823531,0.0009394498,0.003649514,0.00552306,0.009675411],"genre_scores_gemma":[0.1068096,0.003506221,0.8778287,0.0001781645,0.0001515067,0.001404897,0.00545814,0.002650268,0.002012574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.035514,"threshold_uncertainty_score":0.1878182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2099705058052693,"score_gpt":0.5469419897198773,"score_spread":0.336971483914608,"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."}}