{"id":"W2612513584","doi":"10.32920/ryerson.14638212.v1","title":"The Spatial Dimensions of Multi-Criteria Evaluation : Case Study of a Home Buyer’s Spatial Decision Support System","year":2021,"lang":"en","type":"article","venue":"","topic":"Soil and Land Suitability Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Decision support system; Real estate; Spatial analysis; Spatial decision support system; Spatial relationship; Operations research; Decision model; Data mining; Artificial intelligence; Machine learning; Statistics; Mathematics; Business","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001215768,0.00008974383,0.0002182905,0.00002433609,0.0001913517,0.00001772393,0.0001164451,0.00003990324,0.002268204],"category_scores_gemma":[0.0001864725,0.00004924746,0.00009456955,0.0002458749,0.00007400709,0.00006523543,0.0002105572,0.00004908684,0.00002605829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008197861,"about_ca_system_score_gemma":0.00003452612,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04256005,"about_ca_topic_score_gemma":0.1362588,"domain_scores_codex":[0.9981272,0.0003547478,0.0004713218,0.0002602093,0.0006559027,0.0001306136],"domain_scores_gemma":[0.9989971,0.0002725941,0.0001107425,0.0004746951,0.00008650559,0.0000583808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002012817,0.003463901,0.6817556,0.0000393029,0.0002048921,0.0005699949,0.01019389,0.006281373,0.006092136,0.00001147503,0.0003022905,0.2908838],"study_design_scores_gemma":[0.003320432,0.0006025382,0.3772817,0.00002432095,0.0005934127,0.0003256005,0.0721434,0.5428361,0.002407498,0.00006210211,0.000154556,0.0002482908],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970503,0.00001831326,0.002152756,0.0000243889,0.0001577547,0.0002884787,0.000008212279,0.0000099656,0.0002897707],"genre_scores_gemma":[0.9995217,0.00000314519,0.0003390303,0.000008169328,0.00001236389,0.00002042236,0.000005562105,0.000004598922,0.00008495703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5365548,"threshold_uncertainty_score":0.9986439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03110271982148725,"score_gpt":0.304571128403659,"score_spread":0.2734684085821718,"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."}}