{"id":"W4381797951","doi":"10.1525/elementa.2022.00145","title":"Gene-editing technologies for developing climate resilient rice crops in sub-Saharan Africa: Political priorities and space for responsible innovation","year":2023,"lang":"en","type":"article","venue":"Elementa Science of the Anthropocene","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"New Partnership for Africa's Development; Japan International Cooperation Agency; Consortium of International Agricultural Research Centers; Alliance for a Green Revolution in Africa","keywords":"Context (archaeology); Productivity; Food security; Green Revolution; Agriculture; Natural resource economics; Developing country; Biotechnology; Agricultural economics; Vulnerability (computing); Climate change; Business; Economic growth; Geography; Biology; Economics; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00883611,0.0006134763,0.0004812598,0.0009218191,0.001524052,0.003969113,0.001043164,0.003418068,0.004176683],"category_scores_gemma":[0.005291062,0.0002974898,0.0005597739,0.0008957816,0.002974935,0.005550193,0.002716268,0.00505918,0.0009924136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002109091,"about_ca_system_score_gemma":0.007111503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00118004,"about_ca_topic_score_gemma":0.001462216,"domain_scores_codex":[0.9980971,0.0007379342,0.00009358993,0.0002243672,0.0004463057,0.0004006951],"domain_scores_gemma":[0.9959961,0.002625643,0.0004063669,0.0001447828,0.0004312634,0.0003958257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000173187,0.0001538039,0.001134341,0.005648473,0.00007716527,0.00186043,0.00477583,0.001827185,0.03996786,0.4839396,0.03581616,0.4246259],"study_design_scores_gemma":[0.00003524735,0.0002150818,0.0008671767,0.002273115,0.00004285843,0.0008610688,0.002803154,0.000768899,0.01015734,0.1049301,0.877001,0.00004491062],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.02137329,0.6912407,0.04535285,0.1776449,0.004222069,0.0002795963,0.0001827952,0.0002415042,0.05946235],"genre_scores_gemma":[0.1742993,0.7455724,0.04479786,0.01614546,0.001848644,0.0004639266,0.0002398918,0.0001069823,0.01652556],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.998476,"threshold_uncertainty_score":0.04673034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02217264805960294,"score_gpt":0.347211236649012,"score_spread":0.3250385885894091,"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."}}