{"id":"W2619644040","doi":"10.30908/bilp.v3i2.179","title":"PENGEMBANGAN EKSPOR PRODUK KOMPONEN OTOMOTIF BERBAHAN BAKU KARET","year":2009,"lang":"id","type":"article","venue":"Buletin Ilmiah Litbang Perdagangan","topic":"Global Trade and Competitiveness","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automotive industry; Natural rubber; Business; Commerce; Government (linguistics); Per capita; Product (mathematics); Raw material; Agricultural economics; International trade; Engineering; Economics; Chemistry; Mathematics; Population","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":[0.0004765502,0.0006389107,0.0004029104,0.0006597347,0.001128029,0.0020181,0.0003396202,0.0005416932,0.03263294],"category_scores_gemma":[0.0003829264,0.0002040628,0.0002989478,0.0005789271,0.0003870617,0.001302487,0.0007876806,0.0009124628,0.008686577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007176337,"about_ca_system_score_gemma":0.001443123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002152141,"about_ca_topic_score_gemma":0.004639843,"domain_scores_codex":[0.9997829,0.00002448242,0.00001697495,0.00003621788,0.00009222708,0.00004725847],"domain_scores_gemma":[0.9997466,0.00004885347,0.00003031975,0.00002063416,0.0001161699,0.00003742148],"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":[0.001671086,0.0007387596,0.01147084,0.001809605,0.00005496399,0.003980005,0.00295089,0.0009817244,0.07088698,0.01617939,0.02220641,0.8670694],"study_design_scores_gemma":[0.0001066348,0.00132612,0.04770324,0.000755913,0.000256058,0.005167755,0.006720161,0.001830708,0.09752085,0.005204212,0.8332639,0.0001445213],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5005137,0.03115003,0.009853917,0.003471234,0.00192503,0.0003542364,0.001368678,0.0006166126,0.4507465],"genre_scores_gemma":[0.6879345,0.01341478,0.01257663,0.0005748891,0.0002162062,0.0000838259,0.001078959,0.0002166301,0.2839037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03263294,"threshold_uncertainty_score":0.1091681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01501336557465286,"score_gpt":0.2184303181391884,"score_spread":0.2034169525645355,"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."}}