{"id":"W4408271169","doi":"10.37648/ijtbm.v14i01.011","title":"Assessing The Role Of Government Subsidies In Boosting Renewable Energy Adoption","year":2024,"lang":"en","type":"article","venue":"INTERNATIONAL JOURNAL OF TRANSFORMATIONS IN BUSINESS MANAGEMENT","topic":"Energy, Environment, and Transportation Policies","field":"Energy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subsidy; Boosting (machine learning); Renewable energy; Business; Government (linguistics); Environmental economics; Natural resource economics; Public economics; Economics; Computer science; Engineering; Market economy; Electrical engineering","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":[0.01147882,0.0003488515,0.0004292868,0.0007974633,0.0006700229,0.002417565,0.0008385245,0.001802131,0.002781474],"category_scores_gemma":[0.05476253,0.0001980988,0.0009006522,0.001399469,0.001557896,0.002572683,0.001543528,0.001877801,0.0004275841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004060016,"about_ca_system_score_gemma":0.003756915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01647002,"about_ca_topic_score_gemma":0.01373379,"domain_scores_codex":[0.9900951,0.006389237,0.0002877931,0.0005515698,0.001050977,0.001625355],"domain_scores_gemma":[0.9547277,0.03260934,0.006730249,0.001135342,0.002733323,0.002063977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001912403,0.001831465,0.8997025,0.0004526595,0.0004136851,0.0004574891,0.002871684,0.01796303,0.0008077138,0.01875595,0.001397213,0.05343414],"study_design_scores_gemma":[0.0001753691,0.003391298,0.9530019,0.0002174312,0.0004310011,0.00008929113,0.009601803,0.01820761,0.001319845,0.006364084,0.007157669,0.00004271674],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906181,0.0003144312,0.0005374741,0.001131334,0.00001550187,0.0000983402,0.0001652864,0.00001176846,0.007107895],"genre_scores_gemma":[0.9992079,0.00008450376,0.0002513093,0.00007736647,0.000005573659,0.00004068618,0.0000326371,0.000002922092,0.0002970895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01647002,"threshold_uncertainty_score":0.0607065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01002200050240562,"score_gpt":0.2542872013962593,"score_spread":0.2442652008938537,"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."}}