{"id":"W4400009452","doi":"10.21203/rs.3.rs-4631146/v1","title":"Inclusive Green Growth Dataset for African Countries","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Sustainable Development and Environmental Policy","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Leverage (statistics); Inclusive development; Inclusive growth; Sustainable development; Context (archaeology); Green growth; Sustainable growth rate; Political science; Business; Development economics; Economic growth; Regional science; Geography; Economics; Computer science; Artificial intelligence; Poverty; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001519577,0.0003580832,0.0003106314,0.0002806811,0.0005210597,0.0002615682,0.001049852,0.0002915302,0.002501262],"category_scores_gemma":[0.0002185479,0.0003282126,0.0001245576,0.0004487973,0.0008019412,0.0001380794,0.01522266,0.0010919,0.003417643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002459569,"about_ca_system_score_gemma":0.0002229587,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009824608,"about_ca_topic_score_gemma":0.0005251009,"domain_scores_codex":[0.9959158,0.0001613437,0.0003145739,0.00101575,0.001462452,0.001130066],"domain_scores_gemma":[0.9986093,0.0003075897,0.00006647018,0.0006851107,0.00003978646,0.0002917545],"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":[0.0001709562,0.0001360435,0.005671658,0.003109759,0.0001216742,0.0001851823,0.003881951,0.0002118551,0.00009195975,0.002118268,0.9796656,0.004635138],"study_design_scores_gemma":[0.0003393498,0.0001801883,0.008605163,0.0002332871,0.00003703075,0.000005398515,0.002834191,0.0005338173,0.0005170059,0.1639465,0.8220968,0.000671231],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5326609,0.004425349,0.0003661577,0.09714398,0.002321528,0.02652838,0.1183218,0.0008656907,0.2173662],"genre_scores_gemma":[0.95913,0.0006726434,0.0009855984,0.0004993791,0.000782014,0.002539696,0.01318894,0.0001808059,0.02202093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4264691,"threshold_uncertainty_score":0.999917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03124344221341264,"score_gpt":0.3613381683755756,"score_spread":0.330094726162163,"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."}}