{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00101068,0.0005759902,0.0006613989,0.0004311095,0.0002111124,0.0008293201,0.0006081957,0.000620566,0.0007182737],"category_scores_gemma":[0.001085546,0.0003661167,0.0007860684,0.0003159808,0.0002324321,0.0006370015,0.0005359296,0.0005339946,0.0001434124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000517936,"about_ca_system_score_gemma":0.0006139799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001107534,"about_ca_topic_score_gemma":0.001687911,"domain_scores_codex":[0.9997519,0.00007331659,0.00001696338,0.00004590547,0.00008854066,0.0000234557],"domain_scores_gemma":[0.9997166,0.0001646469,0.00005542979,0.0000100727,0.0000428374,0.00001049255],"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":[0.0003340159,0.0004666797,0.0008107446,0.000405757,0.00005631001,0.00006059894,0.00002925515,0.8795093,0.08878572,0.002376372,0.0001071283,0.02705806],"study_design_scores_gemma":[0.00002559623,0.0006669341,0.0002648179,0.000008910184,0.00004269806,0.00001675651,0.00001322226,0.9654356,0.03270877,0.00029725,0.0005090685,0.00001042346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4915262,0.001328379,0.502457,0.0001691517,0.00003883204,0.0002385666,0.0001533911,0.0001694457,0.003919039],"genre_scores_gemma":[0.8962599,0.0006734059,0.1013578,0.00003406655,0.000007089156,0.0002659501,0.00008093149,0.00002817948,0.001292705],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001107534,"threshold_uncertainty_score":0.005345106,"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."}}