{"id":"W2110790270","doi":"10.1109/91.917115","title":"Fuzzy models to predict consumer ratings for biscuits based on digital image features","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Food Chemistry and Fat Analysis","field":"Agricultural and Biological Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Fuzzy logic; Inference; Adaptive neuro fuzzy inference system; Fuzzy inference system; Artificial intelligence; Mathematics; Computer science; Fuzzy control system; Fuzzy set; Consumer behaviour; Machine learning; Data mining; Statistics; Advertising","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":[0.0001219738,0.0002285771,0.000264595,0.00003664782,0.0003402728,0.0002514693,0.0002163164,0.0001419066,0.00003507141],"category_scores_gemma":[0.000008735292,0.0001010818,0.0002687618,0.0004270721,0.00004107341,0.0002011868,0.000001021511,0.0001426588,0.00005338373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004163741,"about_ca_system_score_gemma":0.00001055428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001385096,"about_ca_topic_score_gemma":0.0001026683,"domain_scores_codex":[0.9986559,0.00003248523,0.0002711652,0.0004209712,0.0002971222,0.0003224249],"domain_scores_gemma":[0.9992378,0.0002541703,0.00006870952,0.0001250701,0.0001149942,0.0001992114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002281991,0.002187324,0.0002111885,0.0002509638,0.0005611505,0.00004238492,0.0004458007,0.2337475,0.6021664,0.0001890335,0.02409279,0.1338234],"study_design_scores_gemma":[0.01024051,0.01743303,0.001936414,0.002783145,0.001835982,0.0003122124,0.01100965,0.2592714,0.5719759,0.002592782,0.1115975,0.009011427],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8615323,0.0001910138,0.06950358,0.004150702,0.00151191,0.002937302,0.004561794,0.0008308314,0.05478057],"genre_scores_gemma":[0.9959467,0.00000696322,0.00005135135,0.0001915898,0.0001857666,0.0002128747,0.00006216901,0.000003429864,0.003339179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1344144,"threshold_uncertainty_score":0.4121997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02339847312364163,"score_gpt":0.2222280563407276,"score_spread":0.198829583217086,"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."}}