{"id":"W4318069407","doi":"10.1016/j.micpath.2023.106000","title":"Optimization of a natural antimicrobial formulation against potential meat spoilage bacteria and food-borne pathogens: Mixture design methodology and predictive modeling","year":2023,"lang":"en","type":"article","venue":"Microbial Pathogenesis","topic":"Essential Oils and Antimicrobial Activity","field":"Agricultural and Biological Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministère de l'Agriculture, des Pêcheries et de l'Alimentation; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de l'Économie, de l’Innovation et des Exportations du Québec; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation","keywords":"Lactobacillus sakei; Antimicrobial; Food spoilage; Food science; Listeria monocytogenes; Leuconostoc mesenteroides; Meat spoilage; Broth microdilution; Bacteria; Pathogenic bacteria; Salmonella; Microbiology; Biology; Food microbiology; Lactobacillus; Lactic acid; Minimum inhibitory concentration; Fermentation","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005049477,0.0003029841,0.0004712901,0.00007672305,0.0003287711,0.0000871446,0.0001474755,0.0002787414,0.00002191421],"category_scores_gemma":[0.00008340598,0.0001679531,0.0001420962,0.0005126512,0.00009927303,0.000305038,0.0002053173,0.0001336429,0.000001768575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001681308,"about_ca_system_score_gemma":0.00001601087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008792951,"about_ca_topic_score_gemma":0.0000941008,"domain_scores_codex":[0.9980442,0.0004214288,0.0004152007,0.0005856077,0.0001320212,0.0004015557],"domain_scores_gemma":[0.9992764,0.0001735912,0.0002116364,0.00007822984,0.0001675401,0.00009259002],"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":[0.0002317738,0.00004134129,0.000084979,0.00004697106,0.00004140792,0.000007577549,0.0001495247,0.04375984,0.9460925,0.00001013608,0.00004877136,0.009485156],"study_design_scores_gemma":[0.00101676,0.0005293627,0.01309466,0.0001671992,0.0002272758,0.00008368258,0.0001872545,0.3293126,0.6544058,0.0001132318,0.00007885903,0.0007833728],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982242,0.0001376994,0.01570413,0.0003770578,0.0003138612,0.000437892,0.0006823397,0.0001000771,0.000004926847],"genre_scores_gemma":[0.9888523,0.0004811801,0.009659871,0.0000952242,0.0002813252,0.000006965398,0.0005967334,0.000007891394,0.00001853555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2916868,"threshold_uncertainty_score":0.6848928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03326041319377584,"score_gpt":0.2235415447272226,"score_spread":0.1902811315334468,"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."}}