{"id":"W3124648382","doi":"10.2139/ssrn.3675236","title":"Generalizable and Robust TV Advertising Effects","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Advertising; Business; Computer science","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":[0.008687514,0.0009573491,0.001123183,0.001556776,0.0006415567,0.002398518,0.001872285,0.001939135,0.03398839],"category_scores_gemma":[0.05504231,0.0007460869,0.001834494,0.001348064,0.001632309,0.002207628,0.001727857,0.002126351,0.003777261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005544673,"about_ca_system_score_gemma":0.000472364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008081837,"about_ca_topic_score_gemma":0.004863913,"domain_scores_codex":[0.9953191,0.001412097,0.0003024508,0.001841473,0.0007566854,0.0003682617],"domain_scores_gemma":[0.9485661,0.03460739,0.002940826,0.01147648,0.001880291,0.0005290211],"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":[0.008316857,0.002673141,0.3142321,0.00276053,0.007555229,0.005047097,0.006306575,0.03487339,0.08536719,0.1555121,0.05102365,0.3263322],"study_design_scores_gemma":[0.0009380259,0.0007822714,0.8197092,0.0002264178,0.003148346,0.002053467,0.001025184,0.0245272,0.01171818,0.1079032,0.02774315,0.0002254782],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7473785,0.005160647,0.1059267,0.003369042,0.0008490332,0.001057346,0.01663714,0.004175762,0.1154458],"genre_scores_gemma":[0.9827431,0.0003567713,0.005938118,0.0004781625,0.000273624,0.0001051472,0.001870518,0.0003961767,0.007838347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03398839,"threshold_uncertainty_score":0.1137025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01205754683887352,"score_gpt":0.2075282126960017,"score_spread":0.1954706658571282,"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."}}