{"id":"W6939274124","doi":"10.6068/dp14baa37e71b64","title":"Trend 2005 - 2012. Statistics Canada. CANSIM: Energy - Nuclear and Electric Power | Country: Canada | Province: British Columbia | Table: Fuel consumed for electric power generation, by electric utility thermal plants | Variable: Diesel | Units: (kilolitres), 2005-2012. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-079.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Electric utility; Electric power; Electricity generation; Electricity; Hydroelectricity; Nuclear power; Economic statistics; Electric power industry; Energy security","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":[0.001571886,0.002254475,0.002608747,0.00752217,0.003092995,0.004597417,0.004576991,0.001443984,0.08889391],"category_scores_gemma":[0.01600195,0.001521064,0.002008383,0.03761216,0.0006403616,0.002346308,0.001973815,0.002874885,0.04959689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04569065,"about_ca_system_score_gemma":0.1114706,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9936993,"about_ca_topic_score_gemma":0.9923746,"domain_scores_codex":[0.9969074,0.000211102,0.000345955,0.0004417691,0.001380289,0.0007134641],"domain_scores_gemma":[0.9748818,0.0009385493,0.0007647571,0.0008031001,0.02146787,0.00114383],"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":[0.00002370666,0.000005257705,0.0009050106,0.0002606362,0.00002304712,0.00000783253,0.00001788929,0.0001191551,0.000009502995,0.0003954198,0.9967002,0.001532243],"study_design_scores_gemma":[0.0001399762,0.000009304835,0.01667006,0.0008704013,0.00007507965,0.00003003869,0.000373968,0.0004670865,0.0001785859,0.0007277373,0.9803813,0.00007641597],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005025931,0.00006332521,0.00002443792,0.0001212894,0.00002782643,0.000009601804,0.9988189,0.00005143651,0.0008328957],"genre_scores_gemma":[0.0009731987,0.0003443606,0.0003547732,0.0001563969,0.00002009317,0.00009725492,0.9939678,0.0001193181,0.003966843],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08889391,"threshold_uncertainty_score":0.3315104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01424046218164472,"score_gpt":0.2182855612795703,"score_spread":0.2040450990979256,"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."}}