{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002922433,0.00049617,0.0002228067,0.003853741,0.003917655,0.002717409,0.0003511005,0.0005998251,0.04228961],"category_scores_gemma":[0.00114154,0.0002016545,0.0003353201,0.008436787,0.0008089769,0.001249016,0.0006690848,0.001047353,0.002504196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01935026,"about_ca_system_score_gemma":0.03468232,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9387853,"about_ca_topic_score_gemma":0.974737,"domain_scores_codex":[0.9995375,0.00002053126,0.00001385606,0.0000605671,0.0001832923,0.0001843415],"domain_scores_gemma":[0.9988301,0.00008025066,0.0001135558,0.00004298701,0.0007751373,0.0001578987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003341689,0.00006773778,0.09917182,0.0005370798,0.0001139174,0.0005334883,0.00168274,0.001152672,0.001499376,0.1104624,0.5578424,0.2266022],"study_design_scores_gemma":[0.00002734857,0.00003487596,0.1561876,0.0001924823,0.00008096247,0.0001201978,0.002886906,0.0002951466,0.001616589,0.002607238,0.8359196,0.00003096263],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.1616645,0.01481332,0.001600371,0.02262229,0.001266428,0.0001251579,0.03061944,0.0006187982,0.7666698],"genre_scores_gemma":[0.3256921,0.009489737,0.00216661,0.002598202,0.0001625975,0.00006521311,0.008597048,0.0001461827,0.6510823],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.06121469,"threshold_uncertainty_score":0.1414729,"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."}}