{"id":"W3206820907","doi":"10.3386/w10997","title":"Vertical Integration and Technology: Theory and Evidence","year":2004,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Global trade and economics","field":"Economics, Econometrics and Finance","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Economic and Social Research Council","keywords":"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":[0.005091652,0.0006406583,0.001013249,0.005686393,0.001046982,0.004969552,0.001329409,0.002072831,0.01617445],"category_scores_gemma":[0.03223492,0.0005441043,0.0007672913,0.01168614,0.005522229,0.0053675,0.002602746,0.001706392,0.001095804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001736354,"about_ca_system_score_gemma":0.001482847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01603676,"about_ca_topic_score_gemma":0.006797583,"domain_scores_codex":[0.996383,0.0009917835,0.0003248416,0.0007802086,0.001136993,0.0003832447],"domain_scores_gemma":[0.8718455,0.09379938,0.02569957,0.002523118,0.00475345,0.00137897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002916536,0.0002689883,0.6505396,0.002159765,0.0007832459,0.001222256,0.002597875,0.005249158,0.0003341855,0.1862642,0.003382004,0.1469071],"study_design_scores_gemma":[0.0001975891,0.0004313567,0.6886435,0.005935831,0.0009951204,0.001543458,0.01070052,0.01513173,0.001262327,0.2295669,0.0454487,0.0001429741],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7463381,0.1249395,0.01591459,0.01847398,0.0002135609,0.00008860261,0.001569425,0.00006487599,0.09239741],"genre_scores_gemma":[0.9708944,0.02552704,0.001290901,0.0005415776,0.0002553771,0.00001642815,0.0004737585,0.00001096706,0.0009895478],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01617445,"threshold_uncertainty_score":0.05410886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4563443875708618,"score_gpt":0.4647706463511088,"score_spread":0.008426258780246942,"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."}}