{"id":"W4231860724","doi":"10.4018/9781591403845.ch009.ch000","title":"An Object-Oriented Approach to Managing Fuzziness in Spatially Explicit Models Coupled to a Geographic Database","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; University of Toronto","funders":"","keywords":"Computer science; Fuzzy logic; Data mining; Schema (genetic algorithms); Component (thermodynamics); Spatial database; Database; Geographic information system; Spatial analysis; Database schema; Information retrieval; Database design; Artificial intelligence; Geography; Cartography","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":[0.004492279,0.0006650831,0.001028755,0.001494008,0.001389063,0.007591217,0.003599089,0.001513805,0.002742416],"category_scores_gemma":[0.007126366,0.001002515,0.001795446,0.004279184,0.002277541,0.009142922,0.004388326,0.003345628,0.0004902439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00234197,"about_ca_system_score_gemma":0.00199652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004794892,"about_ca_topic_score_gemma":0.005760992,"domain_scores_codex":[0.9979222,0.0006439718,0.0002020028,0.0003160849,0.0008357954,0.00007995666],"domain_scores_gemma":[0.9971358,0.00167432,0.0001592361,0.0007179657,0.0002304946,0.00008221533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000293853,0.00005311813,0.0008930485,0.0002065016,0.00009070674,0.0002564389,0.001258416,0.05512866,0.001831936,0.8411579,0.003028707,0.09606516],"study_design_scores_gemma":[0.00001878142,0.00003125305,0.000364314,0.0001334554,0.00008440658,0.0003208113,0.0004495796,0.3186757,0.002414014,0.5963548,0.08111187,0.00004108524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003558211,0.0005618559,0.991045,0.0006684219,0.00003705058,0.00004647499,0.00006171191,0.0002300568,0.00379128],"genre_scores_gemma":[0.06461576,0.001567187,0.9280061,0.0002389061,0.0000606896,0.0001750731,0.0002857211,0.000114045,0.004936547],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007591217,"threshold_uncertainty_score":0.02375776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03083068491506055,"score_gpt":0.2422700244964724,"score_spread":0.2114393395814118,"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."}}