{"id":"W7055946104","doi":"","title":"Economic benefits of wind energy in Ontario in the spotlight","year":2015,"lang":"en","type":"other","venue":"","topic":"Advanced Frequency and Time Standards","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Wind power; Renewable energy; Climate change; Economic impact analysis; Work (physics); Government (linguistics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004313099,0.0001657247,0.0001521289,0.0007377621,0.002446309,0.002147972,0.0004209328,0.0005920248,0.01417719],"category_scores_gemma":[0.002107392,0.0001831461,0.0002406823,0.002368265,0.0006034968,0.0005961958,0.0007765006,0.0006280153,0.0006345604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04559159,"about_ca_system_score_gemma":0.05265983,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9875403,"about_ca_topic_score_gemma":0.9970424,"domain_scores_codex":[0.999151,0.00004837728,0.00001966154,0.00003059398,0.0004016457,0.0003486903],"domain_scores_gemma":[0.9985505,0.0001095148,0.0001095823,0.00003600665,0.0007197307,0.0004746037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001493112,0.0002482352,0.2305352,0.0006348127,0.000151576,0.001721894,0.004283445,0.007318394,0.001854075,0.1187523,0.482697,0.15031],"study_design_scores_gemma":[0.000105067,0.00007577604,0.4395957,0.0002114434,0.00005330295,0.0002126207,0.00678381,0.003182895,0.0007023825,0.004413638,0.5446042,0.00005918385],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4848576,0.003850567,0.0006087566,0.02054138,0.0002887073,0.0001648732,0.03090848,0.00009081468,0.4586888],"genre_scores_gemma":[0.7437033,0.003028343,0.0004323829,0.000949062,0.00007697918,0.0000407586,0.004378414,0.00004894607,0.2473419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04559159,"threshold_uncertainty_score":0.3307917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009893138931104589,"score_gpt":0.2234519679791236,"score_spread":0.2135588290480191,"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."}}