{"id":"W3111401028","doi":"10.1139/cjps-2020-0234","title":"Adoption barriers for precision agriculture technologies in Canadian crop production","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Plant Science","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Precision agriculture; Agriculture; Production (economics); Emerging technologies; Business; Agricultural economics; Crop production; Value (mathematics); Service (business); Quality (philosophy); Agricultural science; Agricultural engineering; Natural resource economics; Environmental science; Marketing; Computer science; Economics; Geography; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002496311,0.0002067656,0.0002231962,0.001440546,0.002438517,0.002195795,0.0008304102,0.0003540828,0.003044355],"category_scores_gemma":[0.008919634,0.0002212288,0.0003984593,0.004169423,0.0008866633,0.0009096781,0.0008769011,0.0005879376,0.0001658116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04644764,"about_ca_system_score_gemma":0.05186836,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9867091,"about_ca_topic_score_gemma":0.9921609,"domain_scores_codex":[0.9970553,0.0001755837,0.0001467188,0.0003154232,0.001753373,0.0005536478],"domain_scores_gemma":[0.9920291,0.001546346,0.00151162,0.0002185322,0.004026046,0.000668472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002525128,0.00008624283,0.8032143,0.00086826,0.0000795614,0.0008663089,0.04525713,0.001021887,0.006492869,0.005139374,0.007193757,0.1295279],"study_design_scores_gemma":[0.000005570067,0.00004188848,0.9599231,0.0001762369,0.00002184235,0.000107246,0.01702315,0.0006915659,0.0005825435,0.0001518362,0.02123563,0.00003934912],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9769763,0.001427622,0.0006107924,0.002383705,0.00002159576,0.00009744488,0.001681855,0.00002667554,0.01677402],"genre_scores_gemma":[0.9938584,0.001684468,0.0006346057,0.0001618313,0.000002887848,0.00003280236,0.000394519,0.000009827923,0.003220795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04644764,"threshold_uncertainty_score":0.3370028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0154771981398,"score_gpt":0.1906685389452956,"score_spread":0.1751913408054956,"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."}}