{"id":"W2149649017","doi":"10.1109/tpwrs.2011.2146796","title":"Large-Scale Solar PV Investment Models, Tools, and Analysis: The Ontario Case","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Electric Power System Optimization","field":"Engineering","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Photovoltaic system; Investment (military); Sizing; Profit (economics); Operations research; Computer science; Return on investment; Environmental economics; Engineering; Mathematical optimization; Industrial engineering; Economics; Microeconomics; Mathematics; Electrical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000326354,0.0002575779,0.0003219631,0.0002811353,0.000235765,0.0001144634,0.0001460774,0.0001434761,0.00006620253],"category_scores_gemma":[9.143214e-7,0.0002075013,0.0001373648,0.0005910429,0.00002411111,0.000370271,0.000001374725,0.0002944786,0.00002635539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002566131,"about_ca_system_score_gemma":0.0000358114,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00425745,"about_ca_topic_score_gemma":0.0181468,"domain_scores_codex":[0.9986042,0.000103322,0.0004235533,0.0002915504,0.0002312109,0.0003461709],"domain_scores_gemma":[0.9991418,0.0000427249,0.00006040055,0.0005594244,0.00006132701,0.0001343147],"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":[0.00002036439,0.0001486753,0.0000749249,0.00005762977,0.00168051,0.0001227614,0.03106518,0.9659935,0.00003619563,0.0002824933,0.0004280258,0.00008973487],"study_design_scores_gemma":[0.0005985827,0.0001342749,0.0001273804,0.0000526025,0.001161286,0.0007687691,0.002219552,0.9912881,0.0008243331,0.00002902502,0.002327499,0.000468578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0149711,0.000642738,0.9752991,0.000008739466,0.001240593,0.0005418119,0.00008000188,0.0002912074,0.006924732],"genre_scores_gemma":[0.9983442,0.00003629406,0.0004381021,0.00004781556,0.000008034634,0.0001735561,0.000005971228,0.00004345835,0.0009025951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.983373,"threshold_uncertainty_score":0.9997694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02260273309979893,"score_gpt":0.1901624172439537,"score_spread":0.1675596841441548,"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."}}