{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007328399,0.0003015228,0.0002545245,0.001203537,0.0004625017,0.0008733823,0.0009477215,0.0003789026,0.001075067],"category_scores_gemma":[0.002754166,0.0001724387,0.0004050153,0.002398052,0.00046228,0.0004380191,0.0007358781,0.0003562163,0.0002142513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002901216,"about_ca_system_score_gemma":0.001836249,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.644877,"about_ca_topic_score_gemma":0.6938539,"domain_scores_codex":[0.9995845,0.0001786175,0.00001189594,0.00009219094,0.00006848539,0.00006422328],"domain_scores_gemma":[0.9990195,0.0003059306,0.0001309692,0.0001571804,0.0002990114,0.00008732553],"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":[0.0002017604,0.000181148,0.6378233,0.0001281353,0.0003313864,0.0003649045,0.001575346,0.3044402,0.002764859,0.0050945,0.006824047,0.04027039],"study_design_scores_gemma":[0.00001988521,0.00005451684,0.3392131,0.00003144456,0.00006149001,0.00007207569,0.002821646,0.6484961,0.0009409999,0.001536966,0.006673102,0.00007869193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9809074,0.00009541003,0.01217975,0.0002284559,0.00001148006,0.0000546032,0.004635594,0.000179748,0.001707599],"genre_scores_gemma":[0.9872512,0.0000538302,0.00641237,0.00003481192,0.000005600838,0.00003174398,0.00563188,0.00002179534,0.0005567987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.355123,"threshold_uncertainty_score":0.7144287,"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."}}