{"id":"W4409212521","doi":"10.3390/ijgi14040159","title":"Quantitative and Spatially Explicit Clustering of Urban Grocery Shoppers in Montreal: Integrating Loyalty Data with Synthetic Population","year":2025,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Organic Food and Agriculture","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Cluster analysis; Grocery shopping; Loyalty; Advertising; Population; Grocery store; Marketing; Business; Geography; Computer science; Sociology; Artificial intelligence; Demography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002591074,0.00008971864,0.0001531245,0.00008259894,0.00004283113,0.00008677811,0.0003153116,0.00005083396,0.00001797453],"category_scores_gemma":[0.0002162793,0.00003600466,0.00002812925,0.0001815551,0.00001880867,0.001702644,0.00009241901,0.0001133178,8.226542e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005005802,"about_ca_system_score_gemma":0.00001817692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000868928,"about_ca_topic_score_gemma":0.005586187,"domain_scores_codex":[0.9990388,0.00003011712,0.0004958991,0.00007383884,0.0002778439,0.00008351475],"domain_scores_gemma":[0.9990615,0.0001341196,0.0004527948,0.000038028,0.0002858621,0.00002765715],"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":[0.003162184,0.0003209239,0.2025854,0.0002179462,0.0004860774,0.00002008077,0.006234559,0.003083497,0.03477794,0.007840049,0.002957013,0.7383143],"study_design_scores_gemma":[0.001158675,0.0008895346,0.959623,0.001710002,0.00005328246,0.00008124094,0.01129803,0.02162249,0.00109937,0.000413392,0.001787808,0.0002631135],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958393,0.00007142025,0.001081295,0.0021779,0.0002008708,0.00009802371,0.00006386814,0.000007109336,0.0004602093],"genre_scores_gemma":[0.9988703,0.00006520683,0.0007543899,0.0001046545,0.00005147668,9.744589e-7,0.000142876,4.240768e-7,0.00000972522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7570376,"threshold_uncertainty_score":0.3117223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01025368200930183,"score_gpt":0.2384098773010819,"score_spread":0.2281561952917801,"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."}}