{"id":"W6920440860","doi":"10.6068/dp14ba88d8fc72","title":"Trend 1997 - 2012. Statistics Canada. CANSIM: International Trade - Merchandise Imports | Country: Canada | Table: Merchandise import and export, customs-based price index, weighted for all countries and the United States, by Standard International Trade Classification (SITC) | Variable: Animal and vegetable oils and fats, Import, All countries, Paasche current weighted | Units: 2007=100, 1997-2012. 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":"Economic statistics; Official statistics; Census; International comparisons; Summary statistics; Statistical analysis; Descriptive statistics; International Standard Industrial Classification; Price index","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":[0.001540406,0.002402062,0.002375461,0.00844015,0.002753107,0.004874155,0.004328942,0.001342149,0.0888086],"category_scores_gemma":[0.01526695,0.001507703,0.001914666,0.04183367,0.0006061282,0.002442097,0.002077169,0.002806454,0.05858675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04020833,"about_ca_system_score_gemma":0.1071104,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9923595,"about_ca_topic_score_gemma":0.990155,"domain_scores_codex":[0.9966144,0.0001925094,0.000347393,0.000483214,0.001581211,0.0007812677],"domain_scores_gemma":[0.9726448,0.0009749464,0.0008813759,0.0008015156,0.02358371,0.001113805],"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":[0.00002067883,0.000005409452,0.0009140691,0.0002236498,0.00001983771,0.000006698575,0.00001735736,0.0001052203,0.000008753611,0.0003664864,0.9969392,0.001372715],"study_design_scores_gemma":[0.0001194997,0.000009407389,0.01784439,0.0007125376,0.0000592666,0.00002432888,0.0003748596,0.0003916525,0.0001715778,0.0005790418,0.9796428,0.00007058032],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004792759,0.00004152782,0.0000181414,0.00008296915,0.00002061108,0.000009297462,0.9989027,0.00005027762,0.0008265321],"genre_scores_gemma":[0.0006355562,0.0002203084,0.0002745019,0.0001071311,0.00001329532,0.00007505938,0.9951781,0.00008726177,0.003408753],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0888086,"threshold_uncertainty_score":0.2970943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02541969749947635,"score_gpt":0.2666097543590482,"score_spread":0.2411900568595718,"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."}}