{"id":"W3183263547","doi":"10.20944/preprints202107.0457.v1","title":"Integrated Phenotypic-Genotypic Analysis of Latilactobacillus sakei from Different Niches","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Collaborative Innovationcenter of Food Safety and Quality Control in Jiangsu Province; Government of Jiangsu Province; Xinjiang Production and Construction Corps; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Lactobacillus sakei; Biology; Genome; Genetics; Ecological niche; Genetic diversity; CRISPR; Genotype; Gene; Evolutionary biology; Bacteria; Ecology; Lactic acid; Population","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":[0.0002989475,0.0006090309,0.0005550258,0.001341728,0.0002932554,0.0006938812,0.0002122938,0.0002980144,0.0006572109],"category_scores_gemma":[0.0005729888,0.0001390406,0.0005932802,0.001677358,0.000189644,0.0002777824,0.0005726606,0.0003590272,0.0003195989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001624037,"about_ca_system_score_gemma":0.0002732935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001277403,"about_ca_topic_score_gemma":0.001248297,"domain_scores_codex":[0.9995199,0.0000559542,0.00007103098,0.0001161947,0.0001560703,0.00008076822],"domain_scores_gemma":[0.9996879,0.00005859289,0.00006450641,0.00002935706,0.0001098209,0.00004980298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002553748,0.0001042054,0.01048047,0.0000988623,0.00002726947,0.0001156123,0.0002227665,0.0001041194,0.9818385,0.00003673529,0.00005422784,0.006661849],"study_design_scores_gemma":[0.00002102085,0.001368718,0.4530385,0.00004130424,0.0003197042,0.001556032,0.001468245,0.002167008,0.5360134,0.0001691872,0.00376978,0.00006721856],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925978,0.0005481932,0.001703252,0.0000739863,0.00001989035,0.00004785728,0.003793287,0.00007005222,0.001145625],"genre_scores_gemma":[0.9831883,0.0005176126,0.006321697,0.00005416699,0.00001742706,0.0000985285,0.008695526,0.00004294285,0.001063717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001341728,"threshold_uncertainty_score":0.002539933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1052769452110021,"score_gpt":0.329916527048325,"score_spread":0.2246395818373228,"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."}}