{"id":"W6920279271","doi":"10.6068/dp14ba8ba5a7758","title":"Trend 1986 - 1996. Statistics Canada. CANSIM: International Trade - Merchandise Imports | Country: Canada | Table: Merchandise imports and exports balance of payments and customs-based price and volume indexes for all countries | Variable: Balance of payments basis, Metals in ores, concentrates and scrap, imports, Paasche current weighted | Units: 1986=100, 1986-1996. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-131.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Balance of payments; Official statistics; Economic statistics; Census; Payment; Balance (ability); Publication; Price index; Balance of trade; Summary statistics","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"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001799055,0.001485786,0.002538134,0.0003689268,0.0001614825,0.000270705,0.001232442,0.0005471529,0.0005138662],"category_scores_gemma":[0.0002346748,0.001401935,0.000001058601,0.0005187418,0.001082121,0.0007112685,0.0007326711,0.0007513798,7.295902e-7],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003666147,"about_ca_system_score_gemma":0.01072715,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9928836,"about_ca_topic_score_gemma":0.9794684,"domain_scores_codex":[0.991282,0.000524567,0.002516832,0.002235193,0.002099877,0.001341465],"domain_scores_gemma":[0.9927006,0.001092865,0.003046853,0.001906841,0.0001303956,0.001122453],"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":[0.000744671,0.0002866184,0.02824409,0.002543765,0.001041271,0.0005594721,0.0000190384,0.000006696201,0.00002740398,0.0002017235,0.9661719,0.0001533803],"study_design_scores_gemma":[0.00447379,0.0001687443,0.0006427961,0.0004463606,0.001181917,0.0001792704,0.0001477618,0.007710476,0.000002149572,0.000002667954,0.9837466,0.001297398],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008547328,0.03460579,0.00001608484,0.000009621705,0.0007090768,0.002259075,0.9622039,0.00005341849,0.00005755166],"genre_scores_gemma":[0.002263894,0.01238096,0.0003658348,0.0001815644,0.0001113134,0.0001213333,0.9841036,0.0003564556,0.000115012],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02760129,"threshold_uncertainty_score":0.9997891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02123349249609278,"score_gpt":0.2631498254504496,"score_spread":0.2419163329543568,"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."}}