{"id":"W2047113388","doi":"10.4018/jagr.2013010101","title":"Intra-Urban Analysis of Commercial Locations A GIS-Based Approach","year":2013,"lang":"en","type":"article","venue":"International Journal of Applied Geospatial Research","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Downtown; Geography; Cluster analysis; Multitude; Cartography; Regional science; Environmental resource management; Computer science; Environmental science; Archaeology","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":[0.0004760212,0.0005570966,0.0003920969,0.006578354,0.0005223964,0.002005111,0.0006440777,0.0003590031,0.003190226],"category_scores_gemma":[0.001571882,0.0002667158,0.0005428187,0.006040518,0.0004764841,0.0009489929,0.001111069,0.0003421458,0.001072137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006950374,"about_ca_system_score_gemma":0.0008205982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01252386,"about_ca_topic_score_gemma":0.01610925,"domain_scores_codex":[0.9992658,0.0002259642,0.00005537099,0.0001438831,0.000245819,0.00006301713],"domain_scores_gemma":[0.9991597,0.0002305279,0.00007870335,0.0001526538,0.0003322306,0.00004614956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004083533,0.0006787801,0.207061,0.0005660893,0.0004859343,0.001508456,0.004896776,0.2090872,0.03644586,0.04625044,0.006264356,0.4863468],"study_design_scores_gemma":[0.00002728027,0.0002275958,0.1350778,0.00007836233,0.0001505465,0.000812907,0.008791642,0.7812275,0.01718243,0.01520112,0.04110413,0.000118785],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3802544,0.0002422392,0.5767349,0.0001674233,0.00004812512,0.0006044997,0.005240879,0.001652184,0.03505532],"genre_scores_gemma":[0.7750726,0.0001175267,0.2166285,0.00001671553,0.00001342053,0.0002360158,0.00309322,0.0001212252,0.004700924],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01252386,"threshold_uncertainty_score":0.02490193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04902327472516536,"score_gpt":0.2970345588628868,"score_spread":0.2480112841377214,"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."}}