{"id":"W4285465016","doi":"10.32920/ryerson.14638212","title":"The Spatial Dimensions of Multi-Criteria Evaluation : Case Study of a Home Buyer’s Spatial Decision Support System","year":2021,"lang":"en","type":"preprint","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":"Decision support system; Real estate; Spatial decision support system; Spatial relationship; Spatial analysis; Computer science; Spatial distribution; Operations research; Data mining; Statistics; Artificial intelligence; Business; Mathematics","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":[0.002429356,0.0002316008,0.0005693973,0.00006213492,0.0002237776,0.00005575168,0.0003428831,0.0001678436,0.002398871],"category_scores_gemma":[0.0002467271,0.0001319477,0.0002638926,0.0002080373,0.0001323002,0.00006142179,0.001477408,0.0002130875,0.00001530918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002305976,"about_ca_system_score_gemma":0.00009535886,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1902169,"about_ca_topic_score_gemma":0.2293948,"domain_scores_codex":[0.9962885,0.000724606,0.0009712655,0.0006144545,0.001202055,0.0001990579],"domain_scores_gemma":[0.99786,0.0003748489,0.0003806287,0.001144123,0.0001485372,0.00009185835],"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":[0.0004100783,0.006489448,0.5354367,0.0003395391,0.0009187554,0.0008918539,0.02960928,0.07306744,0.001430434,0.000003868783,0.000288455,0.3511142],"study_design_scores_gemma":[0.002138674,0.0004401146,0.192827,0.0001137932,0.001334962,0.0001606699,0.06731007,0.7348092,0.0003636695,0.0000506752,0.0000337922,0.0004173415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993583,0.00004347743,0.004603853,0.00001860533,0.0005361111,0.0009665357,0.00002882622,0.00001852199,0.000201108],"genre_scores_gemma":[0.9991356,0.00001174753,0.0006205179,0.000005971276,0.00002816833,0.00009768525,0.00003583594,0.00001165199,0.00005278557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6617418,"threshold_uncertainty_score":0.998513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03914427746803859,"score_gpt":0.3160235398876355,"score_spread":0.2768792624195969,"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."}}