{"id":"W2791883888","doi":"10.1016/j.fct.2018.02.040","title":"A microbial identification framework for risk assessment","year":2018,"lang":"en","type":"article","venue":"Food and Chemical Toxicology","topic":"Listeria monocytogenes in Food Safety","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada; Health Canada","funders":"Environment and Climate Change Canada","keywords":"Identification (biology); Organism; Risk analysis (engineering); Biological organism; Risk assessment; Computer science; Biochemical engineering; Product (mathematics); Biotechnology; Biology; Business; Ecology; Biological materials; Engineering; Computer security; Mathematics","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":[0.004515126,0.001585101,0.001054156,0.003509298,0.001098895,0.005203308,0.002627582,0.00223444,0.00544374],"category_scores_gemma":[0.007632535,0.0006440934,0.001890576,0.001641428,0.002450517,0.00493211,0.004140499,0.002000829,0.002040056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00149406,"about_ca_system_score_gemma":0.002732617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004080156,"about_ca_topic_score_gemma":0.002962653,"domain_scores_codex":[0.996962,0.0008501243,0.0002900717,0.0005023254,0.001164035,0.0002314284],"domain_scores_gemma":[0.9960999,0.001487993,0.0003960977,0.0004909801,0.00130887,0.0002160108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005816618,0.0001314256,0.002211628,0.0002754267,0.0001123014,0.000570445,0.0003582117,0.07225318,0.008123362,0.808521,0.004207741,0.103177],"study_design_scores_gemma":[0.00001853961,0.0001030776,0.0005037675,0.0001621632,0.00008361166,0.0003802055,0.0003064611,0.3321583,0.004369876,0.6312509,0.03061158,0.00005167244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001135851,0.0002082344,0.9940122,0.0005670544,0.00003250115,0.00006436948,0.00009501124,0.0002397282,0.003645092],"genre_scores_gemma":[0.09241071,0.0006177606,0.9008511,0.0002991392,0.0001240738,0.0002811372,0.0003948485,0.00008925175,0.004932032],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00544374,"threshold_uncertainty_score":0.02387857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03948805654406656,"score_gpt":0.3469224502046387,"score_spread":0.3074343936605721,"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."}}