{"id":"W2799377177","doi":"","title":"섬유/의류 산업의 FTA 대응전략(한-미, 한-중 FTA를 중심으로)","year":2016,"lang":"ko","type":"article","venue":"한국의류산업학회지","topic":"Energy and Environmental Systems","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"International trade; Clothing; Free trade; Business; China; Market access; Rules of origin; Trade barrier; Treaty; International free trade agreement; Textile industry; Liberalization; Tariff; Commercial policy; Free trade agreement; Economics; Political science; Geography; Agriculture","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":[0.0007954295,0.0003381448,0.0003858747,0.00006242933,0.0007291217,0.0001044722,0.0006750943,0.000422138,0.01113775],"category_scores_gemma":[0.00009887363,0.0002454029,0.0002119767,0.000255616,0.000753163,0.0003481152,0.0002061582,0.0001740933,0.0121721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003674049,"about_ca_system_score_gemma":0.00007351385,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007354144,"about_ca_topic_score_gemma":0.002534062,"domain_scores_codex":[0.9967833,0.0004106013,0.0004726636,0.0006413416,0.0007595889,0.0009325672],"domain_scores_gemma":[0.9985309,0.0002573247,0.00021339,0.0005453885,0.00002508859,0.0004279118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002653188,0.001568698,0.2623381,0.000214032,0.0006263609,0.0002917909,0.024905,0.000086801,0.02925645,0.1527364,0.2477115,0.2799996],"study_design_scores_gemma":[0.0009763425,0.0001425272,0.01670534,0.0002398418,0.00005020806,0.000006354409,0.001938611,0.00001219924,0.0008894386,0.001705278,0.9767249,0.0006089254],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6473669,0.001139132,0.00009562344,0.007025113,0.004502845,0.000391608,0.00008208558,0.0002015697,0.3391952],"genre_scores_gemma":[0.8441128,0.001201711,0.00003894269,0.0003369732,0.00157138,0.00002535612,0.000007186686,0.00003905565,0.1526666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7290134,"threshold_uncertainty_score":0.9999998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0138667001157738,"score_gpt":0.2403385974574245,"score_spread":0.2264718973416507,"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."}}