{"id":"W995150301","doi":"10.1007/978-3-319-06483-3_11","title":"Use of Ontology and Cluster Ensembles for Geospatial Clustering Analysis","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Husky Energy (Canada); University of Calgary; Petro-Canada","funders":"","keywords":"Cluster analysis; Geospatial analysis; Computer science; Ontology; Data mining; Consensus clustering; Correlation clustering; Clustering high-dimensional data; Fuzzy clustering; Information retrieval; CURE data clustering algorithm; Artificial intelligence; Geography; Remote sensing","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007112907,0.0003438859,0.0006766717,0.001170616,0.0001290609,0.0004586026,0.001709081,0.0001855986,0.000004807147],"category_scores_gemma":[0.00008582343,0.0003096107,0.0001551111,0.0004585474,0.00042205,0.0005834862,0.001901322,0.0002047681,0.000001871719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004878152,"about_ca_system_score_gemma":0.00006701214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007787302,"about_ca_topic_score_gemma":0.0009816383,"domain_scores_codex":[0.9974756,0.00003048703,0.0004768604,0.001164301,0.0004217455,0.0004310392],"domain_scores_gemma":[0.9975832,0.0007757379,0.000330175,0.001060055,0.0001596188,0.00009124609],"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":[0.00001368467,0.00001279683,0.0001939095,0.00009636724,0.0001192993,0.000008516612,0.0003085641,0.03928137,0.00003072048,0.01432366,0.00003590832,0.9455752],"study_design_scores_gemma":[0.0002315669,0.0001524462,0.0002819393,0.00007779583,0.00009073922,0.000006980776,6.620925e-8,0.9821427,0.0001306105,0.01397555,0.002563664,0.0003459131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001277329,0.00008712366,0.9981377,0.0004002368,0.0006701076,0.0003620516,0.00001719701,0.00004739828,0.0001504424],"genre_scores_gemma":[0.04811023,0.00003980082,0.9500784,0.001023512,0.0002432292,0.00001030249,0.0000241953,0.000019967,0.00045035],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9452293,"threshold_uncertainty_score":0.9999356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02788286127351197,"score_gpt":0.251473433887401,"score_spread":0.223590572613889,"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."}}