{"id":"W4408182242","doi":"10.1016/j.jafr.2025.101787","title":"The role of generative artificial intelligence in digital agri-food","year":2025,"lang":"en","type":"article","venue":"Journal of Agriculture and Food Research","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture, Food and Rural Affairs; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Generative grammar; Artificial intelligence; Computer science; Business","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007805075,0.0001125612,0.0002233785,0.0000471016,0.0002790185,0.0002049178,0.0003733678,0.0001214142,0.000008292071],"category_scores_gemma":[0.0002151094,0.00002744518,0.0001144747,0.001130522,0.000162743,0.0001975125,0.0001218647,0.0005437944,0.000001878208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002459673,"about_ca_system_score_gemma":0.00002963443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001777868,"about_ca_topic_score_gemma":0.0009431771,"domain_scores_codex":[0.9985186,0.0001167964,0.0004706137,0.0001498531,0.0004548978,0.0002891933],"domain_scores_gemma":[0.9984774,0.0006348494,0.0001418098,0.00003975274,0.0006268572,0.00007932437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000232324,0.0005405928,0.007732721,0.00001760159,0.000199057,0.000009654738,0.0009638044,0.0000185032,0.4324118,0.05321401,0.005267954,0.499392],"study_design_scores_gemma":[0.0003170221,0.008733951,0.2698194,0.0004427934,0.00004643742,0.0001091938,0.05756928,0.00003976148,0.2921264,0.2341963,0.1361435,0.0004558434],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834114,0.008498324,0.000005221299,0.004797733,0.0001100493,0.0001852516,0.00001657045,0.000003901613,0.002971502],"genre_scores_gemma":[0.9988055,0.0005488365,0.00002031847,0.00002615684,0.0004109165,0.000004746044,0.000003879228,4.253703e-7,0.0001791754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4989362,"threshold_uncertainty_score":0.2362546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03685080966578459,"score_gpt":0.2908417282308052,"score_spread":0.2539909185650207,"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."}}