{"id":"W3025990249","doi":"10.1016/j.apenergy.2020.114941","title":"Parameterizing open-source energy models: Statistical learning to estimate unknown power plant attributes","year":2020,"lang":"en","type":"article","venue":"Applied Energy","topic":"Integrated Energy Systems Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Transparency (behavior); Statistical learning; Linear regression; Energy (signal processing); Data mining; Statistical model; Regression analysis; Regression; k-nearest neighbors algorithm; Machine learning; Econometrics; Artificial intelligence; Statistics; Mathematics","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":[0.002392868,0.0008793096,0.0005365711,0.001145999,0.0003970658,0.001185099,0.001610433,0.0008730695,0.0008071897],"category_scores_gemma":[0.01569345,0.0004376167,0.0007983338,0.00150458,0.0006522954,0.001687725,0.0007976574,0.001501934,0.0003700975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00164992,"about_ca_system_score_gemma":0.001750752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05653305,"about_ca_topic_score_gemma":0.06806501,"domain_scores_codex":[0.9989358,0.000480684,0.00005810763,0.0002680464,0.0001954303,0.00006192768],"domain_scores_gemma":[0.9918146,0.005542923,0.0006906369,0.001092992,0.0007693252,0.00008961475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004857426,0.00008968115,0.01031313,0.00004173513,0.00008980346,0.00002921225,0.00004429668,0.9494541,0.000291625,0.001807743,0.001030222,0.03675992],"study_design_scores_gemma":[0.000005255047,0.000007272361,0.001100272,0.000006364735,0.000004816217,0.000006183131,0.0000153062,0.9946215,0.0004225023,0.003400723,0.000401604,0.000008190545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2421615,0.0004220807,0.7481558,0.0006399061,0.00005865328,0.0001069075,0.002636065,0.002837486,0.002981643],"genre_scores_gemma":[0.8942394,0.0001690265,0.1000752,0.0001070614,0.00003478173,0.0001052701,0.004073997,0.0001561056,0.001039074],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05653305,"threshold_uncertainty_score":0.112408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0183929970207907,"score_gpt":0.2235119096019108,"score_spread":0.2051189125811201,"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."}}