{"id":"W4389725893","doi":"10.4324/9781003356837-20","title":"Climate change impacts on agriculture and barriers to adaptation technologies among rural farmers in Southwestern Nigeria","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Energy and Environment Impacts","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Tertiary Education Trust Fund; Queen Elizabeth Scholars; Texas Emerging Technology Fund","keywords":"Agriculture; Climate change; Geography; Climate change adaptation; Adaptation (eye); Agroforestry; Agricultural economics; Environmental planning; Socioeconomics; Environmental science; Economics; Ecology; Archaeology; Psychology","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001287575,0.0003881156,0.0002626383,0.0001486405,0.0001062198,0.00004510379,0.0001786066,0.0004597334,0.0006175833],"category_scores_gemma":[0.00003440973,0.0002955157,0.00005379168,0.00008567,0.0001864774,0.0002393514,0.0003097832,0.0002935418,0.0008786433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002097192,"about_ca_system_score_gemma":0.000003091324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005090088,"about_ca_topic_score_gemma":0.006247185,"domain_scores_codex":[0.9985756,0.00001229232,0.000216668,0.0004255192,0.0003032907,0.0004666596],"domain_scores_gemma":[0.9993705,0.00002916877,0.0001025353,0.0002376521,0.000001628531,0.00025855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008273691,0.0001658917,0.4606015,0.0003071702,0.0003123066,0.0008911084,0.03995435,0.08227396,0.007023819,0.01480413,0.01429391,0.3785445],"study_design_scores_gemma":[0.000950694,0.0008828485,0.9641933,0.001204868,0.0000620439,0.00001306641,0.01487237,0.00009732866,0.0005833046,0.00335614,0.01145613,0.002327929],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.712146,0.00006910575,0.00000877776,0.001413981,0.000320151,0.001178831,0.0001132941,0.0006814492,0.2840684],"genre_scores_gemma":[0.9285914,0.001300577,0.00006235966,0.0005444339,0.00003426397,0.00005988724,0.00006204328,0.0000616945,0.06928337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5035918,"threshold_uncertainty_score":0.9999497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01687328830490802,"score_gpt":0.2057306006109364,"score_spread":0.1888573123060284,"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."}}