{"id":"W4391853976","doi":"10.2139/ssrn.4728247","title":"Machine Learning-Based Platform for the Identification of Critical Generators - Context of High Renewable Integration","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Identification (biology); Context (archaeology); Renewable energy; Computer science; Artificial intelligence; Engineering; Geology; Electrical engineering; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048764,0.0007907411,0.0007449229,0.0006645977,0.0003890485,0.001023523,0.000908546,0.0009282305,0.004918047],"category_scores_gemma":[0.001682331,0.0002618361,0.0003516492,0.000443797,0.0002781505,0.0007704899,0.001009198,0.0009172863,0.001519798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000335345,"about_ca_system_score_gemma":0.0006351979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002428802,"about_ca_topic_score_gemma":0.002348888,"domain_scores_codex":[0.9998189,0.00004648985,0.000009783484,0.0000572367,0.00004384336,0.0000237471],"domain_scores_gemma":[0.9995061,0.0001833593,0.00006866209,0.0000713889,0.0001294413,0.00004108306],"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":[0.0004741414,0.0002646591,0.00591669,0.0001785607,0.0001014458,0.0005381772,0.0001193347,0.8159865,0.02018653,0.01570475,0.003871203,0.136658],"study_design_scores_gemma":[0.000003534825,0.00001285488,0.0002454007,0.00000360097,0.000002679694,0.00001239088,0.00000580846,0.9951692,0.000883802,0.003386219,0.0002720442,0.000002427838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0442176,0.0001750765,0.9476463,0.0001929087,0.00008162289,0.00008166976,0.000496401,0.003730782,0.003377641],"genre_scores_gemma":[0.8779834,0.0001172203,0.1179255,0.00006525892,0.0000750155,0.0001589287,0.0007072993,0.0001062927,0.002860947],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004918047,"threshold_uncertainty_score":0.01645249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02080399964672845,"score_gpt":0.282373591695125,"score_spread":0.2615695920483966,"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."}}