{"id":"W2163249128","doi":"10.4236/jgis.2014.63020","title":"Use of Rough Sets Theory in Point Cluster and River Network Selection","year":2014,"lang":"en","type":"article","venue":"Journal of Geographic Information System","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Voronoi diagram; Triangulated irregular network; Rough set; A priori and a posteriori; Data mining; Point (geometry); Computer science; Convex hull; Cluster (spacecraft); Selection (genetic algorithm); Spatial analysis; Table (database); Inference; Diagram; Artificial intelligence; Regular polygon; Mathematics; Geography; Cartography; Statistics; Terrain; Geometry","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":[0.003180811,0.001028388,0.002248258,0.005987943,0.001529351,0.002762226,0.001746992,0.001080511,0.001112372],"category_scores_gemma":[0.01036998,0.0007040231,0.002660294,0.004713103,0.001430295,0.003042772,0.001685768,0.001416576,0.0003486696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001597922,"about_ca_system_score_gemma":0.001926737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007312675,"about_ca_topic_score_gemma":0.004827249,"domain_scores_codex":[0.9945685,0.001818332,0.0004049069,0.0008230825,0.002159758,0.0002254721],"domain_scores_gemma":[0.9955854,0.002622782,0.000335805,0.0004707616,0.0008495956,0.0001356354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001787339,0.0001023327,0.004440796,0.000598383,0.0004481392,0.000409757,0.0007283693,0.5545163,0.004920365,0.09228049,0.003037707,0.3383387],"study_design_scores_gemma":[0.00004310732,0.00008990319,0.001598122,0.00006372112,0.0001748649,0.0002052881,0.0001854169,0.9218761,0.004254258,0.06553881,0.005862436,0.0001079049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005194363,0.0003457421,0.9932203,0.00009886188,0.00003393121,0.00006577487,0.00004942785,0.0001330453,0.0008585736],"genre_scores_gemma":[0.2836435,0.001127931,0.7130598,0.0001312994,0.0001639397,0.0002977157,0.0003228922,0.00007927432,0.001173571],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007312675,"threshold_uncertainty_score":0.01682198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009445213488910413,"score_gpt":0.1954666520545445,"score_spread":0.1860214385656341,"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."}}