{"id":"W2734399764","doi":"","title":"A fuzzy inference system for diagnosing oil palm nutritional deficiency symptoms","year":2017,"lang":"en","type":"article","venue":"ARPN Journal of Engineering and Applied Sciences","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Palm oil; Fuzzy inference system; Fuzzy inference; Inference system; Computer science; Inference; Palm; Fuzzy logic; Artificial intelligence; Machine learning; Adaptive neuro fuzzy inference system; Fuzzy control system; Food 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00114783,0.0001178349,0.0002328998,0.0001120358,0.0006618755,0.000696539,0.001038598,0.00003959006,1.359835e-7],"category_scores_gemma":[0.0001751991,0.00008781056,0.00006479748,0.0001015674,0.0001204429,0.0004629262,0.00008910797,0.00009573515,0.000001078529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002304327,"about_ca_system_score_gemma":0.00007812047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003519432,"about_ca_topic_score_gemma":4.289371e-7,"domain_scores_codex":[0.9988987,0.0000109063,0.0003052655,0.0002057754,0.0003182422,0.0002610984],"domain_scores_gemma":[0.9989213,0.0003736763,0.0003129027,0.0001864386,0.0000909899,0.0001146336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008003489,0.00003588187,0.00116686,0.0001625264,0.000016926,0.00001012326,0.0002333732,0.0104751,0.004769901,0.9577729,0.00003842057,0.02531005],"study_design_scores_gemma":[0.01738973,0.004728197,0.06580842,0.009036046,0.0002852579,0.00374298,0.003542244,0.7212668,0.01786924,0.1454719,0.006083034,0.004776212],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6306797,0.002634457,0.3463667,0.001170211,0.002145666,0.0001880279,0.000006109382,0.0001076229,0.01670149],"genre_scores_gemma":[0.9853527,0.00003054329,0.01436146,0.00001930031,0.0002085873,0.00001584608,8.53208e-8,0.000003348893,0.000008155197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.812301,"threshold_uncertainty_score":0.6716741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01669801278106612,"score_gpt":0.2311200200049801,"score_spread":0.214422007223914,"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."}}