{"id":"W1999657214","doi":"10.1016/j.renene.2014.01.034","title":"Managing solar-PV variability with geographical dispersion: An Ontario (Canada) case-study","year":2014,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; University of Waterloo","funders":"","keywords":"Photovoltaic system; Dispersion (optics); Environmental science; Software deployment; Meteorology; Production (economics); Solar energy; Geographic information system; Spatial dispersion; Atmospheric sciences; Geography; Cartography; Computer science; Engineering; Geology; Physics; Economics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006997906,0.0002310694,0.0004647488,0.0001474332,0.0003097485,0.0001093237,0.0002421565,0.00009727309,0.0007287834],"category_scores_gemma":[0.00002566388,0.000248056,0.00006920548,0.0001918799,0.0000519715,0.0002541854,0.00009816331,0.0001357612,0.00000882326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004026652,"about_ca_system_score_gemma":0.00008896014,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9953469,"about_ca_topic_score_gemma":0.9982998,"domain_scores_codex":[0.9982703,0.00004533214,0.0004617643,0.0006870837,0.00004950086,0.0004860167],"domain_scores_gemma":[0.9987245,0.00007931533,0.0001917567,0.0007383763,0.00002319533,0.0002428253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009617416,0.0006676439,0.8967003,0.00003844418,0.0001872504,0.0002657129,0.001427617,0.06214672,0.000003207708,0.03636877,0.0006916149,0.001406552],"study_design_scores_gemma":[0.0064822,0.002503016,0.06679247,0.00006423904,0.0001568686,0.001499247,0.006906609,0.2819897,0.0001831863,0.1102945,0.5192209,0.003907031],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9525632,0.00005474387,0.008613314,0.0003809655,0.0003303618,0.0001110713,0.00003986637,0.00004997729,0.03785646],"genre_scores_gemma":[0.9977771,0.00003430481,0.0003040598,0.0005540436,0.0002235319,0.00003052863,0.00003408533,0.00003488801,0.001007468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8299078,"threshold_uncertainty_score":0.9999971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02896427888963808,"score_gpt":0.199214894563637,"score_spread":0.1702506156739989,"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."}}