{"id":"W2890592398","doi":"10.1002/jae.995","title":"Complementarities in automobile production","year":2007,"lang":"en","type":"preprint","venue":"Journal of Applied Econometrics","topic":"Merger and Competition Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Variety (cybernetics); Endogeneity; Productivity; Production (economics); Truck; Stern; Automotive industry; Industrial organization; Assembly line; Business; Engineering; Economics; Computer science; Microeconomics; Econometrics; Automotive engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002990907,0.0002631827,0.00120213,0.006328695,0.00005169507,0.0001326901,0.000511015,0.0002383005,0.002270943],"category_scores_gemma":[0.0001064812,0.000325189,0.0003970689,0.001363236,0.00005329982,0.0001544412,0.0002230184,0.000947047,0.0001735747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006939427,"about_ca_system_score_gemma":0.00008517458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009153567,"about_ca_topic_score_gemma":0.00007232213,"domain_scores_codex":[0.9964139,0.00001136702,0.002689746,0.000456379,0.0001037098,0.0003248674],"domain_scores_gemma":[0.9966785,0.00007555261,0.002608694,0.0004038575,0.0001048324,0.0001285042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003591043,0.002333681,0.1831054,0.00112488,0.00180232,0.00007580692,0.003357452,0.09614775,0.00002243227,0.6722634,0.02220133,0.01720646],"study_design_scores_gemma":[0.002939101,0.0002721773,0.09053215,0.0002263752,0.000164611,0.00004810707,0.003009732,0.003663871,0.0005423944,0.6034778,0.2930427,0.00208097],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6917552,0.009873992,0.01514355,0.001214937,0.00793716,0.0008218441,0.0003115106,0.00005525746,0.2728865],"genre_scores_gemma":[0.992892,0.001988135,0.003788909,0.0002560758,0.0006424512,0.00001782744,0.00004979423,0.00003407721,0.0003307363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3011368,"threshold_uncertainty_score":0.99992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06817017084836932,"score_gpt":0.2516171462821722,"score_spread":0.1834469754338029,"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."}}