{"id":"W2899911345","doi":"","title":"Are Graphic Warning Labels Stopping Millions of Smokers? A Comment on Huang, Chaloupka, and Fong","year":2018,"lang":"en","type":"article","venue":"Econ journal watch","topic":"Smoking Behavior and Cessation","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Spurious relationship; Psychology; Population; Warning system; Social psychology; Medicine; Environmental health; Computer science; Statistics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039944,0.00009869242,0.000214606,0.000141902,0.0002367154,0.00002450707,0.0000500717,0.00006726129,0.0001864825],"category_scores_gemma":[0.00005953806,0.00008131119,0.00006514247,0.0001257502,0.00006944711,0.0000741191,0.00001799478,0.0003244682,0.000002800794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006232551,"about_ca_system_score_gemma":0.00001914446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001727334,"about_ca_topic_score_gemma":0.000007460609,"domain_scores_codex":[0.9991886,0.00004554233,0.0002528655,0.0001228911,0.0002188167,0.0001712924],"domain_scores_gemma":[0.9992746,0.00004181834,0.0002811209,0.0001283256,0.0001461057,0.0001280333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007656989,0.0001282884,0.9859207,0.00004180612,0.00007705228,0.00004867953,0.002973758,0.000002032675,0.002782568,0.000108512,0.004207743,0.003632334],"study_design_scores_gemma":[0.002048918,0.0008831747,0.9790409,0.001184514,0.0001669413,0.0003440128,0.002635026,0.00005729044,0.006204668,0.0002730567,0.007017066,0.0001444439],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831528,0.0001091178,0.00008394058,0.01591367,0.0003226682,0.00009441123,0.000002916273,0.00001398811,0.0003065118],"genre_scores_gemma":[0.998156,0.0001782247,0.00005656497,0.001165049,0.000272939,0.000002795671,0.000003460942,0.00001405339,0.0001509167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01500322,"threshold_uncertainty_score":0.3315774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04975720014547061,"score_gpt":0.3167850673420708,"score_spread":0.2670278671966002,"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."}}