{"id":"W4235038202","doi":"10.1515/energyo.0008.00007","title":"Green Jobs and Renewable Electricity Policies: Employment Impacts of Ontario’s Feed-in TariffThe authors thank two anonymous referees for helpful comments. The CGE model in use for the quantitative analysis was developed with funding by Environment Canada. The ideas expressed here are those of the authors who remain solely responsible for errors and omissions.","year":2018,"lang":"en","type":"dataset","venue":"energyo","topic":"Canadian Policy and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computable general equilibrium; Renewable energy; Electricity; Economics; Environmental economics; Natural resource economics; Agricultural economics; Operations research; Engineering; Microeconomics","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":[],"consensus_categories":[],"category_scores_codex":[0.002313136,0.0003608649,0.0006589938,0.0001759619,0.001132164,0.00009551277,0.0008471542,0.0002005773,0.000004887861],"category_scores_gemma":[0.00102402,0.0001863605,0.000133857,0.0006944904,0.0005296787,0.0001366842,0.0001589575,0.0002739765,1.737377e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001323057,"about_ca_system_score_gemma":0.003986332,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9965479,"about_ca_topic_score_gemma":0.9998518,"domain_scores_codex":[0.9971356,0.0005778825,0.000563748,0.000465284,0.0005643973,0.0006931011],"domain_scores_gemma":[0.995666,0.002466426,0.0009404062,0.0006604818,0.0001008143,0.0001658144],"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":[0.00128534,0.00006519016,0.00327624,0.00006084121,0.0005496602,9.918399e-7,0.01831494,0.009803518,0.00009292804,0.001867498,0.964641,0.00004185838],"study_design_scores_gemma":[0.001680334,0.0002936423,0.01325762,0.0005494934,0.0008309581,8.658e-7,0.01042206,0.0040061,0.0006490786,0.002304627,0.9654359,0.0005692701],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2622136,0.0007587416,0.0003747168,0.01539963,0.00006088504,0.002572262,0.7186038,0.000004792002,0.00001156277],"genre_scores_gemma":[0.8688841,0.005193321,0.00402709,0.01008214,0.0003396214,0.004425152,0.06333093,0.0002944173,0.0434232],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6552729,"threshold_uncertainty_score":0.8707807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05663291948797705,"score_gpt":0.3167089975428092,"score_spread":0.2600760780548322,"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."}}