{"id":"W1967515268","doi":"10.1191/0142331204tm0104oa","title":"Predicting safety and quality of thermally processed canned foods using a neural network","year":2004,"lang":"en","type":"article","venue":"Transactions of the Institute of Measurement and Control","topic":"Meat and Animal Product Quality","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Guelph","funders":"","keywords":"Artificial neural network; Sensitivity (control systems); Thermal diffusivity; Dependency (UML); Feedforward neural network; Feed forward; Process (computing); Statistics; Approximation error; Computer science; Mean squared prediction error; Mathematics; Artificial intelligence; Engineering; Physics; Control engineering; Thermodynamics","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":[0.0005179481,0.0004412225,0.0002912301,0.0003265478,0.0001264398,0.0003318843,0.0002605427,0.000506444,0.0003141057],"category_scores_gemma":[0.002226783,0.0002303723,0.00032647,0.0002001459,0.0001779518,0.0004378761,0.0001713433,0.0003374614,0.00009919509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004802526,"about_ca_system_score_gemma":0.0003667183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004507507,"about_ca_topic_score_gemma":0.003954585,"domain_scores_codex":[0.999826,0.00004177158,0.00001305649,0.00004115759,0.00006252187,0.00001539377],"domain_scores_gemma":[0.9993508,0.0003246145,0.0001036983,0.00002402008,0.0001807336,0.00001612985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004165034,0.0001227638,0.01784041,0.0001011969,0.00007198528,0.0001271961,0.00004747575,0.8408521,0.04476099,0.0003091509,0.0002394864,0.09511074],"study_design_scores_gemma":[0.000004209443,0.00008148719,0.003510084,0.000004061506,0.00001283431,0.00001504667,0.000003772697,0.9888712,0.007253997,0.0001708769,0.00006617656,0.000006306454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7396155,0.0003573265,0.258391,0.0001030702,0.00002716983,0.00003510897,0.00009944813,0.0003653125,0.001006126],"genre_scores_gemma":[0.9653662,0.0001439525,0.03367664,0.00001599704,0.000007877844,0.00002640826,0.0001080829,0.000008648118,0.0006462109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004507507,"threshold_uncertainty_score":0.008962572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07749762375600149,"score_gpt":0.2500560589637011,"score_spread":0.1725584352076996,"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."}}