{"id":"W7023356908","doi":"","title":"Ontario Cuts Solar, Wind Power Subsidies in Review - Bloomberg","year":2012,"lang":"en","type":"other","venue":"","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Subsidy; Wind power; Power (physics); Government (linguistics); Boom; Renewable energy","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002841091,0.0003005535,0.0004358423,0.0002104288,0.00003325995,0.00005267174,0.001141035,0.000193775,0.01263742],"category_scores_gemma":[0.00002873542,0.0002598207,0.00009097656,0.0003046149,0.00003279457,0.0001242236,0.0003374157,0.0005489372,0.003502717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001077314,"about_ca_system_score_gemma":0.0001452506,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.09310307,"about_ca_topic_score_gemma":0.09453824,"domain_scores_codex":[0.9984788,0.00008182811,0.0002871415,0.0005203999,0.0002596875,0.0003720993],"domain_scores_gemma":[0.9983819,0.00004702591,0.000195829,0.001240949,0.00002353523,0.0001107617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[5.527501e-7,0.0001749908,0.01958062,0.0003344028,0.00004622297,0.000007564684,0.0004588767,0.000002496401,0.000004246333,0.0164756,0.9537729,0.009141492],"study_design_scores_gemma":[0.0001053567,0.000009031225,0.01427521,0.0009086827,0.00001358486,0.000008312504,7.701741e-7,0.00003991591,0.00000212281,0.00009914809,0.9842288,0.0003090365],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00001360929,0.01512652,0.006544611,0.001716282,0.0002443045,0.000571995,0.000002143784,0.000358561,0.975422],"genre_scores_gemma":[0.0006917951,0.001164285,0.03054644,0.001318118,0.00007427592,0.00005490592,0.00001434681,0.0001411911,0.9659947],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.0304559,"threshold_uncertainty_score":0.9999854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01231728174016908,"score_gpt":0.264892721266981,"score_spread":0.2525754395268119,"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."}}