{"id":"W2119297662","doi":"10.1109/pes.2007.386052","title":"Severity Index for Estimating the Impact of Wind Generation on System Vulnerability","year":2007,"lang":"en","type":"article","venue":"IEEE Power Engineering Society General Meeting","topic":"Power System Reliability and Maintenance","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Wind power; Reliability engineering; Electric power system; Probabilistic logic; Computer science; Index (typography); Vulnerability (computing); Vulnerability index; Electricity generation; Vulnerability assessment; Operations research; Engineering; Computer security; Power (physics); Electrical engineering; Artificial intelligence","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":[0.00295084,0.00033285,0.0003831259,0.00004254362,0.0001724263,0.00004599304,0.0002479975,0.0001973258,0.000002112508],"category_scores_gemma":[0.0001981026,0.0002599384,0.0005261241,0.0002602479,0.00004291739,0.0001247387,0.00002329164,0.0003325415,0.000001780104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000630471,"about_ca_system_score_gemma":0.00003405891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001036259,"about_ca_topic_score_gemma":0.000005475452,"domain_scores_codex":[0.9981205,0.00003882271,0.0006589145,0.0003117046,0.0002716675,0.0005983645],"domain_scores_gemma":[0.9987772,0.0003906616,0.0001246956,0.0004479476,0.0001547133,0.0001047954],"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":[0.000009865772,0.00001771608,0.0004389416,0.0003851253,0.0001147264,5.2822e-7,0.0008024549,0.9358467,0.0610656,0.0001106772,0.0008267597,0.0003809144],"study_design_scores_gemma":[0.000292127,0.00006840526,0.002311973,0.000202589,0.00001636106,0.000008441112,0.0001399286,0.9753472,0.02124301,0.000007360732,0.00009087167,0.0002717516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.594075,0.00008422465,0.4032985,0.00001010534,0.001616672,0.0003857244,0.0000221341,0.0002626328,0.000244992],"genre_scores_gemma":[0.9767652,0.000001953832,0.02234903,0.00001455565,0.0007491088,0.00003441352,0.000006801081,0.00006436465,0.00001452909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3826902,"threshold_uncertainty_score":0.9999853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01170471543793501,"score_gpt":0.253194862217748,"score_spread":0.241490146779813,"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."}}