{"id":"W4253122965","doi":"10.1109/tpwrs.2002.804989","title":"Modeling competition in transmission expansion","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Electric Power System Optimization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Transmission (telecommunications); Competition (biology); Electricity market; Investment (military); Lagrangian relaxation; Electricity; Transmission system; Reliability (semiconductor); Power transmission; Industrial organization; Perfect competition; Computer science; Economics; Engineering; Microeconomics; Telecommunications; Mathematical optimization; Electrical engineering; Power (physics); Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.000162698,0.0002312497,0.0002643748,0.0004571456,0.00008609041,0.00005211003,0.0001193692,0.0001846554,0.0001513564],"category_scores_gemma":[0.000001022962,0.000244519,0.00008104723,0.0005475948,0.000009044895,0.0002877262,2.402473e-7,0.0002908632,0.0001663512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002592468,"about_ca_system_score_gemma":0.000007046543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006253455,"about_ca_topic_score_gemma":0.00001440315,"domain_scores_codex":[0.9985233,0.00008602005,0.0005086111,0.0002611733,0.0003008952,0.000320028],"domain_scores_gemma":[0.9995251,0.00003451984,0.00002522587,0.0002760219,0.00004160896,0.00009749866],"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":[0.000007553273,0.00007956898,0.000001635502,0.00007347456,0.00001388439,0.000006895616,0.0006610954,0.9954595,0.002608213,0.00003664643,0.000095218,0.0009563157],"study_design_scores_gemma":[0.0005197256,0.00006396421,0.000002306642,0.000333321,0.00001006329,0.00002997225,0.00009761385,0.996204,0.001987889,0.000003649732,0.0004981811,0.0002493475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01392555,0.0007018011,0.9786081,0.00002657241,0.001870876,0.00044122,0.000007735882,0.000563967,0.00385417],"genre_scores_gemma":[0.9990039,0.0002774492,0.0002425285,0.00001244379,0.00001138134,0.00009720399,0.000003113941,0.00006426992,0.0002876977],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9850783,"threshold_uncertainty_score":0.9971195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01350432313251558,"score_gpt":0.1958320562223181,"score_spread":0.1823277330898025,"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."}}