{"id":"W2019523133","doi":"10.1162/glep_a_00152","title":"South-South Trade and the Environment: A Brazilian Case Study","year":2012,"lang":"en","type":"article","venue":"Global Environmental Politics","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for International Governance Innovation","funders":"","keywords":"Natural resource; China; Economics; International trade; Politics; Commercial policy; Environmental policy; Resource (disambiguation); International economics; Natural resource economics; Geography; Political 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006934817,0.0005150834,0.0004008215,0.00001940023,0.0007150016,0.00007474035,0.000331228,0.0001415955,0.001517703],"category_scores_gemma":[0.00003185128,0.0003853595,0.0001687497,0.0001242135,0.002339101,0.0004847719,0.0009899146,0.0003134587,0.0006565761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001255549,"about_ca_system_score_gemma":0.000005643769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001040011,"about_ca_topic_score_gemma":0.00003684563,"domain_scores_codex":[0.9965563,0.0004038072,0.0004917649,0.0005775238,0.0006848014,0.001285778],"domain_scores_gemma":[0.9981794,0.00007328676,0.0001502681,0.0008122267,3.770026e-7,0.0007844137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003882239,0.0008913161,0.9809614,0.000006380574,0.00005189955,0.000161676,0.01583507,0.00009973419,0.00002735276,0.0003812035,0.0000731972,0.001471901],"study_design_scores_gemma":[0.003440517,0.0002175957,0.9269131,0.000002281301,0.0002370643,0.001757441,0.06306956,0.000223689,0.00004033741,0.0006995584,0.0028161,0.0005827797],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937825,0.0003029128,0.00006751319,0.0002838816,0.0001207316,0.001318678,0.000169011,0.00005075789,0.003904059],"genre_scores_gemma":[0.9984511,0.00001812658,0.0001781899,0.0007948751,0.0001108666,0.00006988188,0.00001097284,0.00003716732,0.0003288771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05404837,"threshold_uncertainty_score":0.9998598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007826430673850499,"score_gpt":0.2292645855695628,"score_spread":0.2214381548957123,"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."}}