{"id":"W4401112153","doi":"10.2139/ssrn.4910785","title":"Scanner Data, Product Churn and Quality Adjustment","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Scanner; Product (mathematics); Quality (philosophy); Computer science; Business; Artificial intelligence; Mathematics; Physics","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":[0.007011958,0.0003898086,0.001074621,0.003150999,0.0007496936,0.004693375,0.001207277,0.002826424,0.007126589],"category_scores_gemma":[0.06262796,0.0006602303,0.0009913893,0.006859437,0.002208506,0.00558133,0.001111683,0.002447945,0.001091871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002391013,"about_ca_system_score_gemma":0.001330955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02387481,"about_ca_topic_score_gemma":0.0193716,"domain_scores_codex":[0.9968077,0.0009097289,0.0003200125,0.0008190883,0.0007784122,0.0003650391],"domain_scores_gemma":[0.8843955,0.06507283,0.03700167,0.006165366,0.004760438,0.002604195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000876919,0.0002485938,0.8900481,0.0002150928,0.0005645574,0.0003782435,0.0005708807,0.02932172,0.0008561622,0.02055462,0.006660895,0.04970425],"study_design_scores_gemma":[0.00008039538,0.0003490924,0.8319159,0.00007952137,0.0004448074,0.0004673195,0.0007034206,0.1088945,0.001032134,0.04885259,0.007060027,0.00012036],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9442326,0.01034211,0.02447684,0.005583629,0.0001759237,0.00008319927,0.002952663,0.0006740159,0.01147895],"genre_scores_gemma":[0.9920781,0.0008823143,0.001348157,0.0001779525,0.0001520543,0.0000115,0.0007967933,0.00007328447,0.004479709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02387481,"threshold_uncertainty_score":0.0474717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09549038705164486,"score_gpt":0.3348817192022203,"score_spread":0.2393913321505754,"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."}}