{"id":"W2189730890","doi":"10.1016/j.sbspro.2015.11.360","title":"An Investigation of the Relationship between the Biomass Energy Consumption, Economic Growth and Oil Prices","year":2015,"lang":"en","type":"article","venue":"Procedia - Social and Behavioral Sciences","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Granger causality; Economics; Distributed lag; Biomass (ecology); Causality (physics); Consumption (sociology); Interdependence; Energy consumption; Oil consumption; Econometrics; Economy; Agricultural economics; Ecology; Biology; Engineering; Political science","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":[0.0004887124,0.0001786971,0.0002160652,0.001277035,0.000309412,0.0008123082,0.0001552,0.0002752776,0.002442972],"category_scores_gemma":[0.002863758,0.0001056351,0.000454033,0.0020847,0.000224164,0.0006545999,0.0003296124,0.0006421997,0.0002392337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006691256,"about_ca_system_score_gemma":0.001023851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0211556,"about_ca_topic_score_gemma":0.02302197,"domain_scores_codex":[0.9997217,0.00006247619,0.00002127848,0.00005260692,0.00008157816,0.00006023411],"domain_scores_gemma":[0.9979855,0.001133185,0.0004587751,0.00005369892,0.0002656821,0.0001031775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008621608,0.0001063177,0.9715137,0.00007054746,0.0001561115,0.0004336294,0.0002939203,0.002052714,0.0003789169,0.00598855,0.001074483,0.01784489],"study_design_scores_gemma":[0.000009038313,0.00005671975,0.9807944,0.00004025187,0.0001225224,0.000101145,0.001502728,0.01028354,0.0007049062,0.002324287,0.004049156,0.0000112985],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876019,0.0008443656,0.001836787,0.0006671759,0.00002800335,0.00001404034,0.001190497,0.00001544455,0.007801795],"genre_scores_gemma":[0.9964839,0.0005189428,0.0004914714,0.00003167482,0.00002077289,0.000007958473,0.0009796039,0.000003809598,0.001461827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0211556,"threshold_uncertainty_score":0.04206491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.154595448872248,"score_gpt":0.2912127950075101,"score_spread":0.136617346135262,"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."}}