{"id":"W6920178525","doi":"10.6068/dp14ba899b1b17","title":"Trend 1986 - 1996. Statistics Canada. CANSIM: International Trade - Merchandise Exports | Country: Canada | Table: Merchandise imports and exports balance of payments and customs-based price and volume indexes for all countries | Variable: Customs basis, Other cereals and cereal preparations, imports, Laspeyres fixed weighted | Units: 1986=100, 1986-1996. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-130.","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; Balance of trade; Price index; Balance (ability); Payment; Summary statistics; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001441444,0.002478874,0.002256353,0.008880273,0.002671684,0.004548238,0.004319857,0.001210879,0.0816156],"category_scores_gemma":[0.01169279,0.001520484,0.001621135,0.04073221,0.0006129301,0.002319343,0.001879551,0.002854291,0.06400746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03598982,"about_ca_system_score_gemma":0.08689199,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9898036,"about_ca_topic_score_gemma":0.9876558,"domain_scores_codex":[0.9968473,0.0001608409,0.0002970722,0.000454541,0.001502632,0.0007375178],"domain_scores_gemma":[0.9785663,0.0007280631,0.0008933485,0.0007005155,0.01814458,0.0009671299],"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":[0.00002111723,0.000006035626,0.0008675248,0.0001961687,0.00001645012,0.000007427756,0.0000169003,0.0001187673,0.000009793985,0.0003464044,0.9971686,0.001224861],"study_design_scores_gemma":[0.0001028606,0.000008344463,0.01738811,0.0005741416,0.00004125917,0.00002219533,0.0003270307,0.000351529,0.0001815648,0.0004182222,0.9805284,0.00005625735],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005042982,0.00003457068,0.00001590969,0.00005668847,0.0000168946,0.000007415025,0.9990183,0.00004518327,0.0007545376],"genre_scores_gemma":[0.0004890111,0.0001577437,0.0001799937,0.00005570806,0.000009235143,0.00005001959,0.99576,0.00006586681,0.003232483],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9183844,"threshold_uncertainty_score":0.2730314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02004918351501306,"score_gpt":0.2605196296392204,"score_spread":0.2404704461242073,"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."}}