{"id":"W2885202036","doi":"","title":"Brazil doubles magnesia exports in Q1","year":2017,"lang":"zh","type":"article","venue":"Industrial Minerals","topic":"Economic Zones and Regional Development","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Magnesium; Value (mathematics); Agricultural economics; Economics; Geography; Environmental science; Business; Mathematics; Metallurgy; Statistics; Materials science; Archaeology","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":[0.001264907,0.0004625401,0.001185375,0.0004401614,0.0004443382,0.0007203331,0.0009596833,0.0007961243,0.001685079],"category_scores_gemma":[0.0003170809,0.0005387276,0.0002749593,0.0001246515,0.0002518875,0.0006727472,0.0003564961,0.0005363805,0.001301745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003371345,"about_ca_system_score_gemma":0.0002309642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003684821,"about_ca_topic_score_gemma":0.0007524462,"domain_scores_codex":[0.9962533,0.00003415427,0.001864056,0.0009664196,0.000078363,0.0008036909],"domain_scores_gemma":[0.9969336,0.00008336114,0.001570222,0.001112657,0.0000376663,0.0002624319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000202408,0.0003865368,0.6707812,0.00005757471,0.0001639604,0.0002663229,0.0004868892,0.0001278379,0.00004737049,0.1818363,0.1321763,0.01346734],"study_design_scores_gemma":[0.006548509,0.0001329657,0.2514716,0.0003054881,0.00001814551,0.00002696127,0.0001206985,0.0001936259,0.00004876189,0.02310933,0.7168862,0.001137738],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7202986,0.002555842,0.000006139916,0.01400599,0.007949012,0.000636397,0.0001912126,0.0000198514,0.254337],"genre_scores_gemma":[0.864624,0.00126473,0.0001409884,0.000386534,0.002560603,0.00005933728,0.00004294107,0.00005863531,0.1308622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5847099,"threshold_uncertainty_score":0.9997064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.126945042060442,"score_gpt":0.2743938799923096,"score_spread":0.1474488379318676,"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."}}