{"id":"W3144425440","doi":"10.5194/egusphere-egu21-15356","title":"Welfare impacts of livelihood diversification strategies in response to rainfall variability - A case study of Northern Ghana","year":2021,"lang":"en","type":"article","venue":"","topic":"Land Rights and Reforms","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Red Deer Polytechnic","funders":"","keywords":"Livelihood; Diversification (marketing strategy); Agricultural diversification; Agriculture; Poverty; Irrigation; Agricultural productivity; Farm income; Vulnerability (computing); Agricultural economics; Natural resource economics; Business; Food security; Climate change; Economics; Geography; Economic growth; Agronomy; Ecology; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0003760271,0.00006284798,0.000139938,0.00001202136,0.00006619775,0.00001403867,0.00008097839,0.00004314412,0.0001667194],"category_scores_gemma":[0.00006763317,0.00001199819,0.00003560535,0.0003238745,0.00001830593,0.00007741006,0.00005458604,0.00003687572,0.000001245011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001702028,"about_ca_system_score_gemma":0.00002801383,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02305965,"about_ca_topic_score_gemma":0.2053899,"domain_scores_codex":[0.9992626,0.0001588745,0.0002058892,0.0001605835,0.0001124203,0.00009963187],"domain_scores_gemma":[0.9995745,0.0001169088,0.00005552256,0.00006305015,0.0001363516,0.00005363072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001533605,0.006389014,0.6531578,0.00007690429,0.00009793713,0.001070037,0.03662996,0.0001012878,0.2584585,0.000455449,0.00001887464,0.04201058],"study_design_scores_gemma":[0.0002814357,0.0008195225,0.8630399,0.0000100572,0.000007505652,0.00002769542,0.13404,0.000007935518,0.001436928,0.0001251162,0.0001279796,0.00007597606],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984782,0.000004670413,0.000001294513,0.001077204,0.00002167301,0.0002093552,0.00002058203,0.000007993504,0.0001790085],"genre_scores_gemma":[0.999832,0.000002880271,0.00002066933,0.00001018059,0.00001174648,0.000002763804,0.000006038178,2.580699e-7,0.0001134629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2570216,"threshold_uncertainty_score":0.9834459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01868891527922787,"score_gpt":0.2396375492907863,"score_spread":0.2209486340115584,"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."}}