{"id":"W4399828433","doi":"10.32920/26060836.v1","title":"TD Bank Site Selection Using a Multi-criteria Decision Approach","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Selection (genetic algorithm); Site selection; Computer science; Business; Artificial intelligence; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.009414955,0.001501372,0.001783107,0.007879271,0.001865448,0.005564848,0.001960053,0.001636554,0.009243064],"category_scores_gemma":[0.01207975,0.0008426171,0.001989274,0.005829599,0.0008983253,0.001342446,0.002034885,0.001262939,0.0005572575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006820579,"about_ca_system_score_gemma":0.009494458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0191381,"about_ca_topic_score_gemma":0.03119272,"domain_scores_codex":[0.9898884,0.006601848,0.0005612169,0.0006620738,0.00163821,0.0006482707],"domain_scores_gemma":[0.9898532,0.006924072,0.000475791,0.0001739698,0.001919242,0.0006537418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0009600336,0.001121925,0.01122266,0.003297142,0.0007689298,0.001522353,0.005310541,0.6532446,0.006162798,0.0346222,0.007172735,0.2745941],"study_design_scores_gemma":[0.0002557863,0.001131275,0.005119305,0.0006296759,0.0003138767,0.0001458454,0.006378504,0.946695,0.002664139,0.02459734,0.0119055,0.0001637453],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2467957,0.001517918,0.6997982,0.001649168,0.0001485893,0.01082052,0.001637802,0.0003968557,0.03723522],"genre_scores_gemma":[0.5608178,0.00053605,0.4303412,0.0001318275,0.00004014712,0.002639542,0.0006272743,0.00005560687,0.004810568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0191381,"threshold_uncertainty_score":0.04979163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04462225035652506,"score_gpt":0.3066435307790858,"score_spread":0.2620212804225608,"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."}}