{"id":"W3196059277","doi":"","title":"US, Canada, Mexico lift steel, aluminum tariffs pressuring China","year":2019,"lang":"en","type":"article","venue":"FOXBusiness","topic":"Global trade and economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Lift (data mining); China; International trade; Business; Political science; Computer science; Law","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001831974,0.0002225686,0.0004817502,0.00009652702,0.00008911565,0.0001026009,0.0004120173,0.0001237892,0.001319351],"category_scores_gemma":[0.00003074633,0.0002733486,0.000082749,0.0002044354,0.00002607862,0.000369033,0.000110128,0.0001674753,0.001141169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001945427,"about_ca_system_score_gemma":0.0001099304,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.323879,"about_ca_topic_score_gemma":0.0892115,"domain_scores_codex":[0.998441,0.000006753061,0.0005213885,0.0005036394,0.00004533346,0.0004818743],"domain_scores_gemma":[0.9990532,0.00002020993,0.0002566787,0.0005246051,0.00002601,0.0001192356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002670033,0.00007217446,0.9269215,0.0001229174,0.0001079473,0.0000149741,0.0001425743,0.004048582,0.0000298358,0.05366325,0.01455784,0.0002916417],"study_design_scores_gemma":[0.000478117,0.00001714412,0.6008598,0.00001865964,0.000005601516,0.000006898227,0.00002178241,0.002688735,0.0001323068,0.001776428,0.3936148,0.0003797842],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9146664,0.0009506641,0.00007193226,0.0008228983,0.002339013,0.000231065,0.0002507098,0.00004246284,0.08062492],"genre_scores_gemma":[0.9939845,0.00005916689,0.000149171,0.0006273494,0.0001875306,0.00001052955,0.00004832919,0.00003901949,0.004894381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3790569,"threshold_uncertainty_score":0.9999719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01985947711094273,"score_gpt":0.1657238037348651,"score_spread":0.1458643266239224,"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."}}