{"id":"W6976473311","doi":"10.6068/dp16e3c880daa41","title":"TREND: World Trade Organization. WTO Annual Trade in Merchandise and Services: Merchandise - Imports | Country: Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burma, Burundi, Cambodia, Cameroon, Canada, Cape Verde, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo (Brazzaville), Congo (Kinshasa), Cook Islands, Costa Rica, Cote D'Ivoire, Croatia, Cuba, Cyprus, Czech Republic, Denmark, Djibouti, Dominica, Dominican Republic, East Germany, East Timor, Ecuador, Egypt | Indicator: Textiles, 1980 - 2017. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 082-001-003","year":2019,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Commodity; European union; Balance of payments; World trade; Balance of trade; Product (mathematics); Payment; Statistical analysis","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":[0.001072695,0.001961188,0.001526545,0.004220054,0.0005931652,0.002972436,0.002113082,0.00139963,0.07045008],"category_scores_gemma":[0.007158944,0.0008409643,0.001287876,0.01600912,0.0004060959,0.002493332,0.001312066,0.002978112,0.09247667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001694587,"about_ca_system_score_gemma":0.004012646,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05828978,"about_ca_topic_score_gemma":0.04552984,"domain_scores_codex":[0.9987828,0.0001449649,0.0002324546,0.0003012561,0.0003136472,0.0002248916],"domain_scores_gemma":[0.9959809,0.0006022616,0.0006374018,0.0004691521,0.002009608,0.0003007569],"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":[0.00003103743,0.000009622101,0.0006200829,0.0002970057,0.00001666045,0.000007945858,0.00001174734,0.00009615988,0.0000313604,0.0003030022,0.9972677,0.001307682],"study_design_scores_gemma":[0.0002277462,0.00001231521,0.007385543,0.0004517426,0.00003247878,0.00002607927,0.0001566381,0.0002439274,0.0001359574,0.0008881181,0.9904132,0.0000261727],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005075396,0.00002067446,0.00002224289,0.00005443563,0.00002802582,0.000006933365,0.9993206,0.00005778992,0.0004384582],"genre_scores_gemma":[0.0002937095,0.00006601809,0.0001606197,0.00004671371,0.00001173009,0.00006908252,0.9985496,0.00005719386,0.0007453516],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9417102,"threshold_uncertainty_score":0.235679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01236073482988219,"score_gpt":0.2382213645829822,"score_spread":0.2258606297531,"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."}}