{"id":"W3178949912","doi":"10.21272/mmi.2021.2-24","title":"Impact of environmental innovation on country socio-economic development","year":2021,"lang":"en","type":"article","venue":"Marketing and Management of Innovations","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multicollinearity; Identification (biology); Hausman test; Economics; Descriptive statistics; Production (economics); Manufacturing; Regression analysis; Business; Environmental economics; Econometrics; Panel data; Fixed effects model; Marketing; Statistics; Macroeconomics; Mathematics","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":[0.0008319573,0.0001749204,0.0002826672,0.002436273,0.0002924301,0.00178178,0.0001261778,0.0002413373,0.003819589],"category_scores_gemma":[0.003948481,0.00005632095,0.0005753617,0.003617979,0.0004750652,0.0005715559,0.001254924,0.0003691458,0.0002989763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008749061,"about_ca_system_score_gemma":0.0009489204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003587623,"about_ca_topic_score_gemma":0.003542121,"domain_scores_codex":[0.9989462,0.0003899352,0.00005962943,0.0001174903,0.0002610791,0.0002256327],"domain_scores_gemma":[0.9966381,0.001535314,0.00074991,0.0002354316,0.0005517785,0.0002895215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001039637,0.000106284,0.9306735,0.0002014359,0.0004383971,0.0005818267,0.0004768573,0.008666115,0.0007815183,0.006934009,0.001096933,0.04993914],"study_design_scores_gemma":[0.000004039792,0.00008433413,0.9880877,0.00005444407,0.0001562808,0.0001538626,0.00118254,0.002329048,0.0006666306,0.001462568,0.005802281,0.00001618454],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9678572,0.001400184,0.00106083,0.0004292839,0.00002216069,0.00002753898,0.001666718,0.00003115966,0.02750494],"genre_scores_gemma":[0.9985262,0.0004894722,0.000161559,0.00001486984,0.000007167787,0.000008287174,0.0002700119,0.000003230085,0.0005190645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003819589,"threshold_uncertainty_score":0.01277781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01698181390920755,"score_gpt":0.2160536655871732,"score_spread":0.1990718516779657,"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."}}