{"id":"W4389254227","doi":"10.2139/ssrn.4643819","title":"Make Every Second Count: Time Allocation in Online Shopping","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada); Western University","funders":"","keywords":"Computer science; Count data; Advertising; Business; Statistics; Mathematics; Poisson distribution","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":[0.001804413,0.000396434,0.0006122292,0.001146357,0.001366085,0.003793333,0.001186283,0.001591436,0.04246823],"category_scores_gemma":[0.02477045,0.0004597779,0.000393834,0.001554337,0.0007078231,0.00348149,0.0013475,0.001561799,0.002684104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001304459,"about_ca_system_score_gemma":0.001717439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00850356,"about_ca_topic_score_gemma":0.01071236,"domain_scores_codex":[0.9986125,0.0006646469,0.00006002927,0.0001865415,0.0002628495,0.0002133998],"domain_scores_gemma":[0.9864193,0.00934028,0.0008525205,0.000544856,0.0007536386,0.00208939],"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":[0.0156495,0.008966636,0.16668,0.000318771,0.0002244882,0.0006849027,0.00546784,0.03095204,0.008364314,0.09753732,0.03049242,0.6346618],"study_design_scores_gemma":[0.0008872299,0.002437572,0.3521918,0.0002187335,0.0005133593,0.000911556,0.02248523,0.3536858,0.003687249,0.225866,0.0367591,0.0003563773],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8852817,0.0007420987,0.01123512,0.002782017,0.0003507636,0.0001367704,0.0002892058,0.0002487457,0.09893366],"genre_scores_gemma":[0.9895402,0.00009809465,0.002275578,0.0001365217,0.00006659963,0.00003495798,0.00007050374,0.00007504963,0.00770261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04246823,"threshold_uncertainty_score":0.1420704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01357585328713457,"score_gpt":0.2378564307301551,"score_spread":0.2242805774430205,"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."}}