{"id":"W2901475805","doi":"10.1016/j.cageo.2018.11.003","title":"A novel hierarchical clustering analysis method based on Kullback–Leibler divergence and application on dalaimiao geochemical exploration data","year":2018,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Cluster analysis; Divergence (linguistics); Hierarchical clustering; Geology; Pairwise comparison; Kullback–Leibler divergence; Covariance; Computer science; Measure (data warehouse); Regolith; Data mining; Pattern recognition (psychology); Artificial intelligence; Mathematics; Statistics","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":[0.001817867,0.0006879236,0.001153904,0.003216631,0.001447345,0.001521708,0.001549275,0.0007267715,0.00152065],"category_scores_gemma":[0.003146263,0.000333019,0.001184902,0.003199246,0.0004311009,0.0015806,0.001366096,0.001179071,0.0006991795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001017902,"about_ca_system_score_gemma":0.002397756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01712208,"about_ca_topic_score_gemma":0.02252093,"domain_scores_codex":[0.998598,0.0002676646,0.0001118622,0.0003926137,0.0005447063,0.00008500439],"domain_scores_gemma":[0.9986426,0.0003688529,0.00009372766,0.0001210189,0.0006974158,0.00007641756],"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":[0.0002064924,0.000224941,0.008657377,0.000267814,0.0003875036,0.0002378328,0.0004389856,0.1179854,0.01238709,0.01932554,0.01350888,0.8263721],"study_design_scores_gemma":[0.00001665683,0.00002907153,0.002795182,0.00001118716,0.00003993632,0.0001004241,0.00009813991,0.9841154,0.002416787,0.00668352,0.003639612,0.00005417547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01340429,0.0002690068,0.9838299,0.0001269874,0.00006905109,0.00007037581,0.0003914899,0.001166641,0.0006722653],"genre_scores_gemma":[0.1489208,0.0003157109,0.844128,0.00009241148,0.0001007047,0.0002446131,0.002491696,0.0003137934,0.003392358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01712208,"threshold_uncertainty_score":0.03404486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0540333020122351,"score_gpt":0.3045135062292817,"score_spread":0.2504802042170466,"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."}}