{"id":"W1971522741","doi":"10.1007/978-94-017-2296-4_11","title":"Balancing Consensus and Conflict with a GIS-Based Multi-Participant, Multi-Criteria Decision Support Tool","year":2003,"lang":"en","type":"book-chapter","venue":"˜The œGeojournal library","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Viewpoints; Variety (cybernetics); Computer science; Data science; Management science; Geographic information system; Process (computing); Decision support system; Knowledge management; Perspective (graphical); Operations research; Engineering; Geography; Data mining; Artificial intelligence; 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":[0.01725159,0.0013079,0.001573386,0.002779157,0.001699075,0.005289057,0.003442307,0.002504907,0.007958825],"category_scores_gemma":[0.03718633,0.0006533141,0.0009678362,0.002893033,0.001709067,0.005220636,0.004572134,0.001904023,0.001304647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001481765,"about_ca_system_score_gemma":0.002374603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001199882,"about_ca_topic_score_gemma":0.002718962,"domain_scores_codex":[0.9869558,0.008921125,0.0005672327,0.0006437523,0.002656712,0.0002553974],"domain_scores_gemma":[0.9545405,0.04049375,0.0008342984,0.001044666,0.002644595,0.0004421491],"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":[0.0007530643,0.0004869922,0.002867439,0.001120026,0.0004204684,0.0008163604,0.008221082,0.2855038,0.007152491,0.1641363,0.01228249,0.5162395],"study_design_scores_gemma":[0.0001642695,0.000301632,0.0006416229,0.000211704,0.0001616416,0.0002148255,0.001949165,0.8145839,0.003898646,0.1669402,0.01084416,0.00008826081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02515356,0.0001641634,0.9592456,0.001055301,0.00008180499,0.0002547532,0.00009609136,0.000536333,0.01341246],"genre_scores_gemma":[0.248628,0.0001159342,0.7477357,0.0001730663,0.00004442417,0.0005831045,0.0001400302,0.000120547,0.002459267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01725159,"threshold_uncertainty_score":0.09123617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06173559168562415,"score_gpt":0.2982453743845558,"score_spread":0.2365097826989316,"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."}}