{"id":"W7117250980","doi":"10.21083/caree.v1i1.8947","title":"Assessing AI Adoption in Ontario’s Livestock and Horticulture Sectors: Challenges and Opportunities for Responsible Innovation","year":2025,"lang":"","type":"article","venue":"Canadian Agri-food & Rural Advisory Extension and Education Journal","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Livestock; Agriculture; Government (linguistics); Leverage (statistics); Food security; Bridging (networking); Emerging technologies","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.008256185,0.0002541353,0.0003782566,0.004855466,0.005497228,0.008086288,0.00123627,0.0006034295,0.002091865],"category_scores_gemma":[0.02359496,0.000246316,0.0003632777,0.01315674,0.003886533,0.003347945,0.002188429,0.0008323804,0.0001454657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1142507,"about_ca_system_score_gemma":0.1968557,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9589807,"about_ca_topic_score_gemma":0.9825525,"domain_scores_codex":[0.9917174,0.00123639,0.0004624027,0.0004729812,0.004963254,0.00114753],"domain_scores_gemma":[0.9618625,0.01502383,0.004219884,0.0009136153,0.01587715,0.002102887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001345051,0.0001317557,0.4570101,0.003990551,0.0001759099,0.001358981,0.1359577,0.001652183,0.002389268,0.06579965,0.01189375,0.3195055],"study_design_scores_gemma":[0.00001994484,0.0001090052,0.5908453,0.002674297,0.000165524,0.0002338669,0.2082197,0.001953024,0.001361115,0.006589708,0.1877151,0.0001133519],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7881277,0.01892508,0.004840607,0.03070902,0.0001397619,0.0005148408,0.00274331,0.00007043813,0.1539292],"genre_scores_gemma":[0.978488,0.01176568,0.004040067,0.0006144979,0.00001860223,0.0001134981,0.0004806031,0.00001450515,0.004464637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1142507,"threshold_uncertainty_score":0.8289504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08157660688291629,"score_gpt":0.2748192333670335,"score_spread":0.1932426264841172,"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."}}