{"id":"W2588686417","doi":"10.5539/jas.v9n3p128","title":"Determinants of Use of Information and Communication Technologies in Agriculture: The Case of Kenya Agricultural Commodity Exchange in Bungoma County, Kenya","year":2017,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"ICT Impact and Policies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Commission for Science and Technology; National Commission for Science, Technology and Innovation","keywords":"Information and Communications Technology; Business; Agriculture; Commodity; Market access; Database transaction; Transaction cost; Nonprobability sampling; Marketing; Agricultural economics; Economics; Finance; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003432326,0.000203089,0.0001557233,0.0008264828,0.001900941,0.001141082,0.0003055207,0.0004054406,0.0025407],"category_scores_gemma":[0.001160474,0.000188337,0.0001470381,0.001302058,0.0006012345,0.0006725236,0.0006788491,0.0004322559,0.0001694099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001441233,"about_ca_system_score_gemma":0.001344964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08272649,"about_ca_topic_score_gemma":0.182725,"domain_scores_codex":[0.9996763,0.00009618964,0.00001640631,0.00003741771,0.00004372558,0.000129929],"domain_scores_gemma":[0.999036,0.0003074657,0.0003551473,0.00002091454,0.00009223952,0.0001882207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000483214,0.0002304206,0.9732527,0.0000604025,0.00002107293,0.003438813,0.0148659,0.0001877789,0.001257149,0.000493414,0.0002560103,0.005888027],"study_design_scores_gemma":[0.000003040955,0.00007710254,0.9377999,0.00004381877,0.00001751423,0.0004604361,0.05997173,0.0002954907,0.0001503801,0.00005644672,0.001114682,0.000009442014],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993474,0.00004368011,0.00001152115,0.00007704643,4.239637e-7,0.000007236079,0.0000262521,2.573842e-7,0.0004861501],"genre_scores_gemma":[0.9995264,0.0001066201,0.00004097887,0.00001309261,7.036616e-7,0.000006330534,0.00002360341,2.805526e-7,0.0002820405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08272649,"threshold_uncertainty_score":0.1644899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01996375033290269,"score_gpt":0.2578862001943917,"score_spread":0.2379224498614891,"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."}}