{"id":"W7015675766","doi":"","title":"Thermal Power Canada Market 2017-By Facilitate Decision Making on the Basis of Historic and Forecasts Data 2030 Thermal Power Canada Market 2017-By Facilitate Decision Making on the Basis of Historic and Forecasts Data 2030 Thermal Power Canada Market 2017-By Facilitate Decision Making on the Basis of Historic and Forecasts Data 2030Most Popular StoriesMost Popular StoriesMore>>","year":2018,"lang":"en","type":"other","venue":"","topic":"Advanced Power Generation Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Basis (linear algebra); Power (physics); Decision-making models; Power market; Making-of","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":[0.001129224,0.0009962375,0.0004588451,0.001419102,0.004650949,0.005246252,0.001741279,0.002444361,0.3591006],"category_scores_gemma":[0.003498862,0.000505659,0.0005869945,0.001382891,0.001254424,0.001895875,0.001533779,0.002906235,0.1725275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02024907,"about_ca_system_score_gemma":0.05126217,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8282627,"about_ca_topic_score_gemma":0.9487561,"domain_scores_codex":[0.9977273,0.00005319466,0.00002387725,0.0000884094,0.001680783,0.0004263793],"domain_scores_gemma":[0.9953169,0.0002139773,0.00006834497,0.0001268698,0.003616247,0.0006577741],"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":[0.00004309873,0.00002909406,0.0003415805,0.00002443588,0.000002012786,0.00006025727,0.00003630609,0.0003080702,0.0003921029,0.003874479,0.9815708,0.01331784],"study_design_scores_gemma":[0.00001873043,0.00001073695,0.001393345,0.00003055795,0.000002544591,0.00001497059,0.0001373813,0.0005749493,0.0005877077,0.0007837377,0.996426,0.00001929101],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001321301,0.0002355885,0.001101405,0.003643751,0.0007546666,0.0002043279,0.01312069,0.0009907705,0.9786275],"genre_scores_gemma":[0.007515913,0.0002769262,0.001050815,0.0009995805,0.0001143666,0.0000654461,0.005308175,0.0004279797,0.9842407],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3591006,"threshold_uncertainty_score":0.9141651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0553943326336514,"score_gpt":0.2590545099238176,"score_spread":0.2036601772901662,"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."}}