{"id":"W4400574313","doi":"10.1016/j.jretconser.2024.103993","title":"Untapping the potential of mobile location data: The opportunities and challenges for retail analytics","year":2024,"lang":"en","type":"article","venue":"Journal of Retailing and Consumer Services","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Leverage (statistics); Analytics; Data science; Big data; Raw data; Computer science; Data analysis; Location data; Business; Data mining; Internet privacy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002815594,0.00006112077,0.000153216,0.00006893386,0.0004289809,0.0001799685,0.0002840623,0.00004431657,0.00001055565],"category_scores_gemma":[0.00005946895,0.0000351813,0.00005170783,0.0001276107,0.0003206838,0.0002745523,0.00003779264,0.0001111313,2.297424e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009581181,"about_ca_system_score_gemma":0.0002108239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005936475,"about_ca_topic_score_gemma":0.003758802,"domain_scores_codex":[0.9991498,0.0001525132,0.0003050192,0.0001103386,0.0001930571,0.00008928494],"domain_scores_gemma":[0.9986801,0.0005731567,0.0002048814,0.0001734437,0.0003289252,0.00003953612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.00007453541,0.00006245699,0.001576437,0.002737936,0.001314751,0.000007007517,0.06862548,0.001378584,0.0001438311,0.01530453,0.0001000406,0.9086744],"study_design_scores_gemma":[0.0002951834,0.0001477416,0.001188164,0.0008495409,0.002165479,0.00001950124,0.6112497,0.1338622,0.00006534042,0.004534398,0.2454278,0.0001949484],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6595241,0.3145722,0.001646089,0.02353097,0.0002505548,0.0002294281,0.00002203652,0.00001525836,0.0002092467],"genre_scores_gemma":[0.9420791,0.05753591,0.00004669144,0.00006061635,0.0001636912,0.000003023108,0.000004262574,0.000003751687,0.0001030199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9084795,"threshold_uncertainty_score":0.3299418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1180691593204121,"score_gpt":0.3267590436344417,"score_spread":0.2086898843140296,"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."}}