{"id":"W3217023076","doi":"10.1038/s43016-021-00424-4","title":"On-Farm Experimentation to transform global agriculture","year":2021,"lang":"en","type":"article","venue":"Nature Food","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières; Agriculture and Agri-Food Canada","funders":"H2020 Marie Skłodowska-Curie Actions; National Key Research and Development Program of China; Université de Montpellier; Curtin University of Technology; U.S. Department of Agriculture; Agence Nationale de la Recherche; European Commission","keywords":"Restructuring; Agriculture; Bridge (graph theory); Business; Complexity management; Environmental resource management; Industrial organization; Computer science; Agricultural economics; Natural resource economics; Economics; Marketing; Geography; Finance","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00003886032,0.0001696728,0.0001413954,0.000004114754,0.0001528531,0.00007919924,0.0001650054,0.0003072456,0.0002574752],"category_scores_gemma":[0.00003044772,0.00005338093,0.0001322346,0.0007152678,0.000008309168,0.00004789903,0.00002623556,0.0002501741,0.00008927624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005338969,"about_ca_system_score_gemma":0.00000803235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001335133,"about_ca_topic_score_gemma":0.002324387,"domain_scores_codex":[0.9989306,0.00002917417,0.0001276088,0.0003526077,0.0003083461,0.0002516878],"domain_scores_gemma":[0.9996197,0.00004349172,0.00002646541,0.00004957082,0.0001153363,0.0001454374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001144595,0.0005804558,0.0008080216,0.000008415304,0.00008858412,0.00004489108,0.0003850299,0.00001621944,0.7318541,0.03239638,0.08921248,0.1444909],"study_design_scores_gemma":[0.0004131334,0.001288299,0.1030466,0.0000419278,0.00002621349,0.00005183084,0.001360061,7.206087e-7,0.3714096,0.002864401,0.5190063,0.0004908306],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9628327,0.0008344579,0.000003841517,0.01209859,0.0005545429,0.0002356038,0.0001559634,0.0001082556,0.02317599],"genre_scores_gemma":[0.9920915,0.00001076117,0.0001138799,0.006216534,0.0006801953,0.0000258622,0.0002836064,6.434622e-7,0.0005770691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4297938,"threshold_uncertainty_score":0.2819173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007720277287792365,"score_gpt":0.236843996211362,"score_spread":0.2291237189235697,"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."}}