{"id":"W2776056334","doi":"10.4236/ajcc.2017.64034","title":"Climate Change Induced Vulnerability of Smallholder Farmers: Agroecology-Based Analysis in the Muger Sub-Basin of the Upper Blue-Nile Basin of Ethiopia","year":2017,"lang":"en","type":"article","venue":"American Journal of Climate Change","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Addis Ababa University; Deutscher Akademischer Austauschdienst; International Development Research Centre","keywords":"Agroecology; Livelihood; Adaptive capacity; Vulnerability (computing); Climate change; Food security; Geography; Agriculture; Agricultural diversification; Diversification (marketing strategy); Vulnerability assessment; Social vulnerability; Capital asset; Agroforestry; Socioeconomics; Environmental science; Business; Ecology; Psychological resilience; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002561954,0.0003520127,0.0013298,0.0001476539,0.0002754997,0.00005436357,0.001581826,0.0001958141,0.0003073481],"category_scores_gemma":[0.0004144841,0.0001168766,0.0008623516,0.001412462,0.0008940442,0.0003395889,0.0002423229,0.0005639339,0.000002384906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007277088,"about_ca_system_score_gemma":0.00002362014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003065233,"about_ca_topic_score_gemma":0.006028204,"domain_scores_codex":[0.9962689,0.0009183535,0.001163093,0.0003278042,0.000662646,0.0006592458],"domain_scores_gemma":[0.9940888,0.0006810164,0.00409713,0.0004769613,0.0005135163,0.0001425397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003941306,0.0008624396,0.877405,0.0001119774,0.0001769083,0.00001292054,0.006322999,0.000003671075,0.08057626,0.00005068043,0.00004035093,0.03404267],"study_design_scores_gemma":[0.0004732713,0.001153018,0.9774179,0.0002306278,0.0003857475,0.00001259823,0.007879992,0.00001711873,0.01211114,0.00002665476,0.00008617531,0.0002057873],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908282,0.0001186008,4.004592e-7,0.007588516,0.0001923513,0.0005522614,0.00051683,0.000007858507,0.0001949763],"genre_scores_gemma":[0.9978161,0.0007996337,0.00005208407,0.001054432,0.0002152168,0.00003368084,0.00002210634,0.000005062357,0.000001615713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1000129,"threshold_uncertainty_score":0.4766088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0765137580637811,"score_gpt":0.3063619163344185,"score_spread":0.2298481582706374,"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."}}