{"id":"W4404006093","doi":"10.1016/j.compag.2024.109577","title":"Ground-based on-line weed control using computer vision: Analyzing the inference time-accuracy dilemma","year":2024,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université TÉLUQ; Université du Québec à Montréal","funders":"","keywords":"Line (geometry); Artificial intelligence; Dilemma; Inference; Computer science; Computer vision; Machine learning; Mathematics","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":[0.0002720803,0.0003108094,0.0003040849,0.00002832802,0.0003379956,0.0005977956,0.0003354697,0.0001632997,0.00003785473],"category_scores_gemma":[0.00001340245,0.00008873532,0.0001448105,0.0008304406,0.00006199294,0.000190038,0.00007880638,0.0005440135,0.00001426586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008413229,"about_ca_system_score_gemma":0.00002305019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001703611,"about_ca_topic_score_gemma":0.0002022006,"domain_scores_codex":[0.9983671,0.0001172215,0.0002875852,0.0005143242,0.0002251405,0.0004885874],"domain_scores_gemma":[0.9987658,0.0009157403,0.00007227519,0.00008420859,0.00006824005,0.00009373968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003258317,0.0007243711,0.002003709,0.0001111923,0.0003219029,0.0002378139,0.0005253138,0.1115192,0.2326165,0.01004275,0.04804114,0.5935303],"study_design_scores_gemma":[0.0006443691,0.001183479,0.01549815,0.0004510501,0.00006786202,0.00006212509,0.0000382076,0.8827915,0.0002177487,0.0005336907,0.09789123,0.0006205719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815232,0.004438416,0.004276191,0.008061779,0.0005855254,0.0007245501,0.00002441867,0.0002183318,0.0001476235],"genre_scores_gemma":[0.9972955,0.00009468498,0.0001779735,0.001243995,0.001014734,0.00001798592,0.00008761031,0.000002206765,0.00006529016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7712723,"threshold_uncertainty_score":0.5764557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0130410266631527,"score_gpt":0.2442649051138912,"score_spread":0.2312238784507385,"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."}}