{"id":"W4206313168","doi":"10.5539/jas.v14n2p113","title":"Fish Farmers’ Willingness to Pay for Improved Information and Communication Technologies During COVID-19: A Case of Ibadan, Nigeria","year":2022,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Business; Aquaculture; Willingness to pay; Marketing; Information and Communications Technology; Revenue; Government (linguistics); Scale (ratio); Probit model; Agricultural science; Socioeconomics; Agricultural economics; Economics; Geography; Fishery; Fish <Actinopterygii>; Finance","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":[0.0007492222,0.0002655104,0.0002409351,0.0005458276,0.00234506,0.0008823524,0.0003893939,0.001120742,0.002812613],"category_scores_gemma":[0.00167035,0.0003193409,0.0003029177,0.0006020396,0.0008009166,0.0007809674,0.000728143,0.001122913,0.0001772509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001290644,"about_ca_system_score_gemma":0.0009731909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02214347,"about_ca_topic_score_gemma":0.03484652,"domain_scores_codex":[0.9994747,0.0001626377,0.00003675543,0.00005076726,0.00004919358,0.000225995],"domain_scores_gemma":[0.9990429,0.0003450841,0.0003566087,0.00002467384,0.00006678081,0.0001639309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002370488,0.0006588934,0.8109782,0.0002291369,0.00003212018,0.09490328,0.07557414,0.0002134777,0.002331902,0.001188509,0.001337293,0.01231598],"study_design_scores_gemma":[0.00001625064,0.0005592918,0.5667885,0.0003141399,0.00004682562,0.02487137,0.4021751,0.001134779,0.0005039157,0.0007789592,0.002758674,0.00005219577],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986641,0.00006382178,0.00003087106,0.0005278175,0.000003471153,0.000009499206,0.00001479906,4.056253e-7,0.000685121],"genre_scores_gemma":[0.9991874,0.0002226079,0.00005568542,0.0001270514,0.000004689324,0.00000971883,0.00001159699,5.37292e-7,0.0003806806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02214347,"threshold_uncertainty_score":0.04402912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02453470081784957,"score_gpt":0.2602809323800267,"score_spread":0.2357462315621771,"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."}}