{"id":"W2002847944","doi":"10.1109/tste.2012.2190999","title":"A Simplified Risk-Based Method for Short-Term Wind Power Commitment","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Sustainable Energy","topic":"Power System Reliability and Maintenance","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Wind power; Electric power system; Wind power forecasting; Reliability engineering; Wind speed; Power system simulation; Renewable energy; Probabilistic logic; Computer science; Engineering; Power (physics); Meteorology; Electrical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005347333,0.0002792848,0.0003056329,0.0001798745,0.0002533851,0.00003909681,0.0001726612,0.0001697022,0.00009218934],"category_scores_gemma":[0.00001179961,0.0002718563,0.000233843,0.0002405009,0.00003329043,0.0002306723,0.000001273151,0.000197212,0.000008415722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004572618,"about_ca_system_score_gemma":0.00005267364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001808371,"about_ca_topic_score_gemma":0.00004149053,"domain_scores_codex":[0.9983057,0.0000877264,0.0003179867,0.0002464186,0.0001730453,0.0008690956],"domain_scores_gemma":[0.9988024,0.0003148662,0.00003618342,0.0004984197,0.0001243493,0.0002237347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002129096,0.0005629884,0.00004816899,0.0004421287,0.0002679923,0.000009481013,0.0005387763,0.9763543,0.00120305,0.0047599,0.003074064,0.01252627],"study_design_scores_gemma":[0.002646775,0.0006024564,0.0002905804,0.0001033334,0.0003298143,0.00002098048,0.002489415,0.09929492,0.1919331,0.0009353101,0.6999708,0.001382501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005083701,0.0002318367,0.9904716,0.00005835163,0.001126957,0.000448041,0.00006729106,0.0003578304,0.002154368],"genre_scores_gemma":[0.9930213,0.00002695527,0.00364831,0.0001248274,0.00006708237,0.0004621047,0.000006446975,0.00006959857,0.002573362],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9879376,"threshold_uncertainty_score":0.9999734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01080594825084988,"score_gpt":0.2473877196704994,"score_spread":0.2365817714196496,"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."}}