{"id":"W4317423857","doi":"10.3390/su15031843","title":"Artificial Intelligence Tools and Techniques to Combat Herbicide Resistant Weeds—A Review","year":2023,"lang":"en","type":"article","venue":"Sustainability","topic":"Weed Control and Herbicide Applications","field":"Agricultural and Biological Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Weed; Herbicide resistance; Weed control; Artificial intelligence; Identification (biology); Computer science; Resistance (ecology); Biology; Agronomy; 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":[0.0008604149,0.0001219247,0.0002184417,0.00001629509,0.0002368943,0.00006992788,0.0002238755,0.0000591005,0.0001202963],"category_scores_gemma":[0.0008723913,0.00005222823,0.00006470381,0.0009159572,0.00008702954,0.00008948684,0.000158161,0.0001045149,0.00005521012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001013463,"about_ca_system_score_gemma":0.00002731992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001056423,"about_ca_topic_score_gemma":0.0007762283,"domain_scores_codex":[0.9987571,0.00008753526,0.0003170011,0.0003676531,0.0001529888,0.0003176687],"domain_scores_gemma":[0.9990358,0.0003287287,0.00004688019,0.0001427542,0.0003082303,0.000137605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003508838,0.00004272554,0.0004098649,0.0001953665,0.000004292311,0.000004412902,0.00004924268,7.263112e-7,0.06691526,0.02407821,0.00226939,0.9059954],"study_design_scores_gemma":[0.00001806348,0.000461618,0.3693643,0.0002282066,0.00003376015,0.000003015119,0.001357946,0.0000223693,0.006518201,0.3951683,0.2263816,0.0004426855],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7943354,0.001606277,0.0001150465,0.1995604,0.00003550679,0.002620592,0.00007385394,0.0006813476,0.0009716216],"genre_scores_gemma":[0.9971017,0.0002101722,0.0001257605,0.001718402,0.0001028615,0.0003991265,0.00003347888,0.000001236539,0.0003072079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9055527,"threshold_uncertainty_score":0.2129805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03645516736394608,"score_gpt":0.3018544066841034,"score_spread":0.2653992393201573,"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."}}