{"id":"W60464842","doi":"10.1007/978-3-540-74757-4_2","title":"Introduction to GIS-MCDA","year":2015,"lang":"en","type":"book-chapter","venue":"Advances in geographic information science","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; Western University","funders":"","keywords":"Multiple-criteria decision analysis; Decision analysis; Weighting; Evidential reasoning approach; Decision maker; Management science; Set (abstract data type); Decision problem; Computer science; Decision rule; Business decision mapping; Operations research; Mathematics; Engineering; Artificial intelligence; Algorithm; 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":[0.0004676705,0.000945943,0.0007323442,0.002056113,0.0004967023,0.002110073,0.001081639,0.0007942276,0.0809442],"category_scores_gemma":[0.001476434,0.0005448192,0.0006480733,0.004729448,0.0006906448,0.002576707,0.001173989,0.001933412,0.02411422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001113738,"about_ca_system_score_gemma":0.001036329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002312733,"about_ca_topic_score_gemma":0.004144049,"domain_scores_codex":[0.9996406,0.00006427457,0.00002191962,0.00007228196,0.00018209,0.00001888861],"domain_scores_gemma":[0.9995704,0.0002384645,0.00001032421,0.00004427874,0.0001224601,0.00001397913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008276138,0.00003170623,0.0001269934,0.0005532933,0.00001339486,0.00008070774,0.0001397308,0.005243573,0.0005075271,0.2503944,0.2406923,0.5022081],"study_design_scores_gemma":[0.000002316605,0.000004974639,0.000100094,0.0002044842,0.000003276763,0.0001272535,0.00003808301,0.003115057,0.000233606,0.1329299,0.8632311,0.000009946235],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.0009033013,0.03556922,0.4589576,0.004562445,0.003567958,0.0001364992,0.002792923,0.002364262,0.4911458],"genre_scores_gemma":[0.01985764,0.05214675,0.4740793,0.002721253,0.002077852,0.0003757016,0.004291334,0.002299654,0.4421505],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0809442,"threshold_uncertainty_score":0.2707853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07315424640462181,"score_gpt":0.3994691382206688,"score_spread":0.326314891816047,"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."}}