{"id":"W1776811109","doi":"10.1109/wescan.1991.160525","title":"Electronic recognition of plant species for machine vision sprayer control systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Sprayer; Artificial intelligence; Computer science; Machine vision; Field (mathematics); Machine learning; Control (management); Computer vision; Mathematics; Agronomy; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003281131,0.000274406,0.0002090072,0.0004485834,0.0002576183,0.0009199576,0.0006066412,0.0007615141,0.01481206],"category_scores_gemma":[0.0009577989,0.0001584595,0.0001915016,0.0004195133,0.0002905517,0.0009316409,0.0001948597,0.000379691,0.003298838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004748308,"about_ca_system_score_gemma":0.0003126337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001591426,"about_ca_topic_score_gemma":0.002834649,"domain_scores_codex":[0.9998403,0.00003013417,0.00001408421,0.00002571833,0.00007667283,0.00001311085],"domain_scores_gemma":[0.999684,0.0001152684,0.00001538346,0.00005283803,0.0001194098,0.00001313683],"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":[0.0001978228,0.00008558812,0.0008203603,0.0002993181,0.00002230106,0.0002309377,0.00008361776,0.01243848,0.1637451,0.04113543,0.02534811,0.755593],"study_design_scores_gemma":[0.00007932734,0.0005571832,0.005097071,0.0001770393,0.00006023624,0.001088234,0.0001454214,0.3511455,0.2423465,0.03237708,0.3668527,0.0000736965],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01377142,0.002303499,0.9456544,0.001915807,0.0007775495,0.0001609147,0.0001846525,0.004343192,0.03088856],"genre_scores_gemma":[0.2429032,0.00233601,0.6913325,0.0008806329,0.0003079345,0.0001441467,0.0006541101,0.0001994777,0.06124192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01481206,"threshold_uncertainty_score":0.04955125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02055453809655678,"score_gpt":0.1872777734872934,"score_spread":0.1667232353907366,"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."}}