{"id":"W4387262579","doi":"10.3390/agronomy13102536","title":"“Smart Agriculture” Information Technology and Agriculture Cross-Discipline Research and Development","year":2023,"lang":"en","type":"article","venue":"Agronomy","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Jilin Agricultural University","keywords":"Phenomics; Agriculture; Data science; Precision agriculture; Computer science; Biotechnology; Genomics; Knowledge management; Biology; Ecology","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":[0.000388442,0.0001797677,0.0001704105,0.00008668805,0.0007873462,0.0002799273,0.0001984163,0.0002362137,0.00005635299],"category_scores_gemma":[0.00003716843,0.00005844015,0.00002445833,0.00162186,0.0001806339,0.000519581,0.000383287,0.0002624609,0.0003309439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002812368,"about_ca_system_score_gemma":0.00001121238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005428747,"about_ca_topic_score_gemma":0.000331479,"domain_scores_codex":[0.9986823,0.00003022667,0.0002595669,0.0003121904,0.0002545234,0.0004611987],"domain_scores_gemma":[0.9994173,0.0001114335,0.00006285089,0.00005363896,0.0002187762,0.0001359842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006009952,0.0001977241,0.3674127,0.00008964236,0.0001257814,0.00002348912,0.002761314,0.00001053543,0.1157379,0.01123374,0.1945828,0.3077643],"study_design_scores_gemma":[0.0001273035,0.00007426781,0.5351988,0.00001744836,0.000002521066,0.00001654202,0.001475397,0.000001783223,0.003487219,0.0006774822,0.4587618,0.0001594381],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922897,0.0002885103,0.000002169961,0.004434765,0.0000706014,0.0003328495,0.00001183575,0.0002327381,0.002336873],"genre_scores_gemma":[0.9957212,0.0001232855,0.0003022213,0.0001178463,0.0002534848,0.0001334442,0.0005196129,0.000001029769,0.002827833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3076048,"threshold_uncertainty_score":0.605571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03182323223326641,"score_gpt":0.2751644306940218,"score_spread":0.2433411984607554,"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."}}