{"id":"W2510140102","doi":"10.1080/02255189.2016.1208606","title":"GMOs and poverty: yield gaps, differentiated impacts and the search for alternative questions","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Development Studies/Revue canadienne d études du développement","topic":"Genetically Modified Organisms Research","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Poverty; Yield (engineering); Positive economics; Economics; Political science; Sociology; Epistemology; Development economics; Environmental ethics; Economic growth; Philosophy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0156462,0.0006046586,0.001298889,0.001645139,0.003783025,0.007105728,0.002636908,0.01605755,0.005283559],"category_scores_gemma":[0.03451292,0.0002392051,0.0008310177,0.002793405,0.028638,0.01797813,0.006344634,0.01401529,0.0004680349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01010708,"about_ca_system_score_gemma":0.008009391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01583753,"about_ca_topic_score_gemma":0.02257625,"domain_scores_codex":[0.9918209,0.004005824,0.0003185188,0.000730042,0.002256551,0.0008682291],"domain_scores_gemma":[0.9598034,0.03494088,0.001366924,0.0005266031,0.002735095,0.0006269964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00005646755,0.00001804884,0.0004304135,0.0004893122,0.0000224022,0.0002653704,0.005747555,0.0004848654,0.000147193,0.8833497,0.08092967,0.02805917],"study_design_scores_gemma":[0.00001826769,0.0000258592,0.001179709,0.001507594,0.00001394016,0.0001715669,0.0107751,0.0003723772,0.0001279317,0.7624603,0.2233094,0.0000378239],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.002870503,0.01927026,0.001057036,0.9632632,0.002367835,0.000005106918,0.00004783875,0.000007147465,0.01111113],"genre_scores_gemma":[0.3030016,0.08468919,0.002750202,0.5744193,0.02600789,0.0001074211,0.0001034474,0.00009218266,0.008828844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01605755,"threshold_uncertainty_score":0.08274603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07372453489312733,"score_gpt":0.2521566077363297,"score_spread":0.1784320728432024,"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."}}