{"id":"W6939136193","doi":"10.6068/dp14baa317f8539","title":"Trend 2005 - 2012. Statistics Canada. CANSIM: Energy - Nuclear and Electric Power | Country: Canada | Province: Ontario | Table: Fuel consumed for electric power generation, by electric utility thermal plants | Variable: Canadian heavy fuel oil | 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; Thermal power station; 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":[0.001696082,0.00231929,0.002629563,0.008048679,0.00328752,0.004706125,0.004649899,0.001457603,0.08305512],"category_scores_gemma":[0.01728673,0.001576376,0.002177821,0.03915093,0.0007026136,0.002468311,0.002142665,0.002800858,0.04643416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05008585,"about_ca_system_score_gemma":0.1200812,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9945188,"about_ca_topic_score_gemma":0.9934013,"domain_scores_codex":[0.9965494,0.0002175292,0.0003808988,0.0004910635,0.001574767,0.0007863023],"domain_scores_gemma":[0.9736196,0.000978686,0.0008771253,0.0008948371,0.02243076,0.001199064],"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":[0.00002550356,0.000005146368,0.001013928,0.0002879734,0.00002571437,0.000008563568,0.00002156156,0.0001277469,0.00001048438,0.0004228671,0.9964359,0.00161466],"study_design_scores_gemma":[0.0001372987,0.000009318415,0.01762887,0.0008513795,0.00007764817,0.0000305143,0.000373006,0.0004607417,0.0001687129,0.0007141959,0.9794723,0.00007597208],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000478986,0.00006454945,0.00002509851,0.000121499,0.00002745789,0.000009746924,0.9988878,0.00004909133,0.0007667372],"genre_scores_gemma":[0.0009576042,0.0003498055,0.0003606347,0.0001490339,0.0000203991,0.00009418211,0.9944978,0.0001090481,0.003461523],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08305512,"threshold_uncertainty_score":0.3634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01657663344296056,"score_gpt":0.2181208433859308,"score_spread":0.2015442099429702,"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."}}