{"id":"W6901752878","doi":"10.6068/dp14ba8615dcd2","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, Rapeseed, exports, Laspeyres fixed 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; Price index; Payment; Balance (ability); Balance of trade; International comparisons; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001854072,0.002391976,0.002309842,0.0089931,0.003227656,0.004676228,0.004468089,0.001270093,0.1011936],"category_scores_gemma":[0.01439435,0.001633634,0.001715239,0.04251453,0.0006049111,0.002501594,0.001996417,0.002852921,0.06713737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04566719,"about_ca_system_score_gemma":0.1157762,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9928244,"about_ca_topic_score_gemma":0.9903661,"domain_scores_codex":[0.9961036,0.0002112205,0.0003843015,0.000510057,0.001908026,0.0008827883],"domain_scores_gemma":[0.9714296,0.0008631774,0.0009475693,0.0008448465,0.02470085,0.001213972],"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":[0.00002279787,0.00000631264,0.000950206,0.0002172242,0.00001681404,0.000007247424,0.00001978338,0.0001078432,0.00001044096,0.0004138739,0.9965377,0.00168971],"study_design_scores_gemma":[0.00009994478,0.000009652946,0.02092633,0.0006449121,0.00004563833,0.00002267109,0.0003745655,0.0003413367,0.0001672699,0.0005078665,0.9767959,0.00006387084],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005552028,0.00004139968,0.0000224414,0.00008488363,0.00002313397,0.00001262814,0.9985517,0.00005245988,0.001155726],"genre_scores_gemma":[0.0006927036,0.0002579761,0.0003369231,0.0001038181,0.00001310669,0.00009257172,0.9929282,0.0001012307,0.005473488],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1011936,"threshold_uncertainty_score":0.3385264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0180433004465437,"score_gpt":0.2494321725825739,"score_spread":0.2313888721360302,"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."}}