{"id":"W2885007850","doi":"10.14311/nnw.2018.28.013","title":"AUTOMATIC CLASSIFICATION OF AGRICULTURAL GRAINS: COMPARISON OF NEURAL NETWORKS","year":2018,"lang":"en","type":"article","venue":"Neural Network World","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Agriculture; Artificial intelligence; Computer science; Pattern recognition (psychology); Biology; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008888964,0.0005249425,0.0004274921,0.0009444135,0.000180462,0.0007067146,0.0003809787,0.0006512965,0.0005737065],"category_scores_gemma":[0.001988177,0.0001323721,0.0003369074,0.0006766146,0.0001870877,0.0008534185,0.0002575584,0.000312908,0.0001675711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004298483,"about_ca_system_score_gemma":0.0002406784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004813906,"about_ca_topic_score_gemma":0.003539672,"domain_scores_codex":[0.999633,0.0001052152,0.00002926522,0.0000820873,0.0001178968,0.00003250248],"domain_scores_gemma":[0.9995139,0.0002742733,0.00004394701,0.00003195923,0.0001247143,0.00001122951],"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":[0.0008490968,0.0002521751,0.01179771,0.0002976724,0.0002494894,0.000141288,0.0001284383,0.4629644,0.01296482,0.0020004,0.001275365,0.5070792],"study_design_scores_gemma":[0.000006017835,0.00007786147,0.002996366,0.00001240374,0.00002232688,0.00002098349,0.00004272083,0.9934561,0.00245023,0.0004907815,0.0004145993,0.000009593738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6404575,0.006472243,0.3400011,0.0006876686,0.0003227215,0.00009056184,0.0002822313,0.001308665,0.01037746],"genre_scores_gemma":[0.9585502,0.001018725,0.03871511,0.00005267911,0.00003401342,0.00003687368,0.0002043074,0.00002532469,0.001362736],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004813906,"threshold_uncertainty_score":0.009571791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03175985108969338,"score_gpt":0.3041091928121343,"score_spread":0.2723493417224409,"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."}}