{"id":"W6889114130","doi":"10.25384/sage.21213761","title":"Supplemental Material - Geographies of grocery shopping in major Canadian cities: Evidence from large-scale mobile app data","year":2022,"lang":"en","type":"article","venue":"Sage Journals Data","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mobile apps; Grocery shopping; Analytics; Grocery store; Smartphone app; Mobile device","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001536497,0.0008521796,0.0009253218,0.008166276,0.003016405,0.002926417,0.003057964,0.001353482,0.4482507],"category_scores_gemma":[0.04195753,0.0006504814,0.001021265,0.02441988,0.0004381801,0.0017276,0.001816594,0.00101428,0.05271363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01056758,"about_ca_system_score_gemma":0.02888347,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9157719,"about_ca_topic_score_gemma":0.9613919,"domain_scores_codex":[0.9982975,0.0001099799,0.0001746936,0.0001540064,0.001021801,0.0002420488],"domain_scores_gemma":[0.9237685,0.01327305,0.003417721,0.002156754,0.05460773,0.002776196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00003315521,0.00005932998,0.0116218,0.0006956264,0.0000305464,0.00003267841,0.0001617152,0.0001186735,0.00002943974,0.0004862463,0.9776789,0.009051895],"study_design_scores_gemma":[0.0003582256,0.00006142701,0.3652428,0.002214365,0.0002248354,0.0002499144,0.0032626,0.0006399443,0.0002614678,0.002055,0.6252445,0.0001848089],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.001418754,0.0001809678,0.0001826202,0.0007122602,0.0001095782,0.0002124085,0.9896398,0.0001479638,0.007395695],"genre_scores_gemma":[0.01901709,0.001027069,0.003996838,0.001221308,0.0002016355,0.001219087,0.9463816,0.0002367792,0.02669861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4482507,"threshold_uncertainty_score":0.7870034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05408600560302713,"score_gpt":0.2554125795377085,"score_spread":0.2013265739346813,"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."}}