{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001044724,0.00008266891,0.0001499057,0.000006039114,0.00006945615,0.00002487081,0.000077545,0.00005691865,0.0008466836],"category_scores_gemma":[0.00001457131,0.00002243601,0.0000820288,0.00009618933,0.00001326695,0.00006983502,0.000007151753,0.00004305064,0.00004703973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001080642,"about_ca_system_score_gemma":8.178633e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008311116,"about_ca_topic_score_gemma":0.0002704862,"domain_scores_codex":[0.9993764,0.00002388519,0.0001651917,0.0001361079,0.0001048619,0.0001935513],"domain_scores_gemma":[0.9996592,0.0001599096,0.00006722546,0.00002195869,0.00005880855,0.00003283057],"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":[0.00009462397,0.0002570357,0.001560572,0.00002342411,0.00004431959,9.175762e-7,0.00003230873,0.000008155111,0.9188386,0.002305493,0.05368102,0.02315349],"study_design_scores_gemma":[0.00164199,0.003537364,0.03826894,0.00008155995,0.00009513457,0.00003687933,0.0004686006,0.005803799,0.04198207,0.0007734408,0.9067385,0.0005717383],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859201,0.001477881,0.0001319037,0.00266502,0.0002739918,0.0009410338,0.000719568,0.00009803838,0.007772519],"genre_scores_gemma":[0.996793,0.0001057374,0.00001463954,0.000131332,0.0003304386,0.00002380848,0.0002102449,4.120394e-7,0.00239035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8768566,"threshold_uncertainty_score":0.9270591,"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."}}