{"id":"W6920483343","doi":"10.6068/dp14ba8613f5590","title":"Trend 2002 - 2009. Statistics Canada. CANSIM: Environment - Natural Resources | Country: Canada | Table: Supply and demand of primary and secondary energy in natural units | Variable: Total refined petroleum products, secondary energy, Total transportation | Units: Megalitres, 2002-2009. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-086.","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; Natural resource; Summary statistics; Census; Petroleum; Descriptive statistics; Mineral resource classification; Natural (archaeology)","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":[0.001791312,0.002368348,0.002430454,0.007906925,0.003076531,0.005054852,0.004619801,0.001359153,0.09970003],"category_scores_gemma":[0.01462434,0.001808096,0.002009783,0.04045752,0.0006458668,0.002740201,0.002234391,0.003050624,0.0593522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05648141,"about_ca_system_score_gemma":0.1476768,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9949929,"about_ca_topic_score_gemma":0.9925731,"domain_scores_codex":[0.99595,0.0002369902,0.0004139159,0.0004711497,0.00198796,0.0009399964],"domain_scores_gemma":[0.9690421,0.0009071878,0.0007829745,0.0008287473,0.02707037,0.001368604],"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":[0.00001916204,0.000005758521,0.0008747915,0.0002154587,0.00001767861,0.000007138744,0.00001800701,0.0001236581,0.00001002004,0.0004591522,0.9966478,0.001601378],"study_design_scores_gemma":[0.0001035201,0.000008793291,0.0171918,0.00065387,0.00004888441,0.00002382731,0.0004009387,0.0004750606,0.0001746065,0.0006476929,0.9801983,0.00007261963],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000050655,0.00005241485,0.00003002638,0.0001421163,0.00003602612,0.00001494067,0.9982314,0.00006936486,0.001373136],"genre_scores_gemma":[0.001077646,0.0003844356,0.000532547,0.0001962631,0.00002047753,0.0001338801,0.9907018,0.0001587049,0.006794319],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09970003,"threshold_uncertainty_score":0.4098033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00882255796241612,"score_gpt":0.1888314460506691,"score_spread":0.1800088880882529,"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."}}