{"id":"W2031138951","doi":"10.1016/j.compag.2005.12.001","title":"Application of support vector machine technology for weed and nitrogen stress detection in corn","year":2006,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":182,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Weed; Weed control; Hyperspectral imaging; Support vector machine; Artificial intelligence; Agronomy; Growing season; Machine learning; Mathematics; Computer science; 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":[0.0003748657,0.0002589671,0.0002376171,0.0004008815,0.0001894893,0.0003214862,0.0002138837,0.0002846924,0.0004801302],"category_scores_gemma":[0.0009737256,0.0001435692,0.000180141,0.0004047625,0.0001459554,0.0002930572,0.0001544289,0.0002897843,0.000117026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002186768,"about_ca_system_score_gemma":0.0002673645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003356748,"about_ca_topic_score_gemma":0.003117035,"domain_scores_codex":[0.9998578,0.00003484855,0.00001279349,0.00003358455,0.00004740533,0.00001351634],"domain_scores_gemma":[0.9993782,0.000337337,0.00004929454,0.00002711274,0.0001883554,0.00001961248],"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":[0.0007337886,0.0002031138,0.01305979,0.0002145626,0.00006145024,0.0001280694,0.0001223332,0.05824382,0.3230572,0.0008310226,0.0008129317,0.6025319],"study_design_scores_gemma":[0.00002231761,0.0004205089,0.01562302,0.00001010765,0.00004373378,0.0001076733,0.00006767939,0.8634115,0.1181026,0.0008801552,0.001285499,0.00002512939],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8052827,0.0007608714,0.1911427,0.0002186296,0.00008450451,0.00002757672,0.0001499318,0.0009043693,0.001428827],"genre_scores_gemma":[0.9543912,0.0001712419,0.0446307,0.00001805515,0.000008699642,0.0000110612,0.00006535468,0.00001174543,0.0006920578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003356748,"threshold_uncertainty_score":0.006674409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002770211771245033,"score_gpt":0.2104692686554075,"score_spread":0.2076990568841625,"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."}}