{"id":"W3046421138","doi":"10.5539/jsd.v13n4p235","title":"Evaluating the Energy Metabolic System in Sri Lanka","year":2020,"lang":"en","type":"article","venue":"Journal of Sustainable Development","topic":"Energy and Environment Impacts","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sustainability; Greenhouse gas; Renewable energy; Energy intensity; Natural resource economics; Energy supply; Per capita; Sri lanka; Environmental economics; Business; Efficient energy use; Energy (signal processing); Economics; Environmental science; Ecology; Environmental planning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0004306156,0.000550562,0.0003719835,0.0006140331,0.0004237294,0.002043439,0.0004595301,0.0005651272,0.001649872],"category_scores_gemma":[0.0007227345,0.000282477,0.0009993388,0.0007988547,0.0003849598,0.0009677417,0.0008089557,0.0004256133,0.0001808145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002856556,"about_ca_system_score_gemma":0.001758837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08133136,"about_ca_topic_score_gemma":0.04764449,"domain_scores_codex":[0.9997463,0.00009894989,0.00001852603,0.00003843089,0.00003767039,0.00006019344],"domain_scores_gemma":[0.9997749,0.00008809142,0.00003167331,0.00001124638,0.00007811407,0.00001599693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00006548806,0.00003719346,0.01756697,0.0001177018,0.00007897116,0.0003376769,0.0001050069,0.9701928,0.001875001,0.003333464,0.0003804697,0.005909306],"study_design_scores_gemma":[0.00002481658,0.0003299414,0.02521598,0.00006644757,0.000128337,0.0001214778,0.00111418,0.9652129,0.00188901,0.002902183,0.002933094,0.00006159137],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9631367,0.0005736829,0.01468188,0.0004115628,0.00002154909,0.0001302813,0.001044714,0.0001412131,0.01985825],"genre_scores_gemma":[0.9963876,0.000285169,0.001783245,0.00001678909,0.000001778546,0.00003770135,0.000473388,0.000006768265,0.00100744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08133136,"threshold_uncertainty_score":0.1617159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02452880582223524,"score_gpt":0.2481185781614966,"score_spread":0.2235897723392613,"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."}}