{"id":"W4281618286","doi":"10.1089/forensic.2022.0007","title":"Microbial Forensics: A Present to Future Perspective on Genomic Targets, Bioinformatic Challenges, and Applications","year":2022,"lang":"en","type":"article","venue":"Forensic Genomics","topic":"Forensic and Genetic Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Canada","funders":"","keywords":"Microbiome; Metagenomics; Human microbiome; Human Microbiome Project; Computational biology; Biology; Identification (biology); Human health; Data science; Evolutionary biology; Computer science; Bioinformatics; Genetics; Ecology; Medicine; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002084113,0.0002209697,0.0002000242,0.0001164145,0.0003237632,0.0000350895,0.0002975852,0.00008637931,0.00003308443],"category_scores_gemma":[0.00001228506,0.0002251352,0.00009456278,0.00009574235,0.0001097703,0.000003797261,0.0006571208,0.0002096761,0.00004273729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001842629,"about_ca_system_score_gemma":0.0001726471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005180238,"about_ca_topic_score_gemma":0.0001136548,"domain_scores_codex":[0.9985839,0.00005567006,0.0002296232,0.0005143093,0.0002047582,0.0004117634],"domain_scores_gemma":[0.9990718,0.00001387249,0.00007276059,0.0005581526,0.00009438355,0.0001889698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002191067,0.0004042502,0.0001339149,0.0002693788,0.0008754106,0.00002604044,0.01893911,0.01283387,0.1009714,0.1098382,0.1754135,0.578104],"study_design_scores_gemma":[0.0009935185,0.001619371,0.002387699,0.000005360703,0.00003204661,0.0001169217,0.01838047,0.0002114289,0.00483218,0.006500187,0.9643906,0.0005302487],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9407041,0.02919114,0.002983665,0.009332614,0.000944031,0.004443119,0.0006750677,0.00006713968,0.01165914],"genre_scores_gemma":[0.9726815,0.005289597,0.01196815,0.002788967,0.003617726,0.001206352,0.0008806436,0.0001512911,0.001415718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7889771,"threshold_uncertainty_score":0.9180745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01033972442267826,"score_gpt":0.2440643622485393,"score_spread":0.2337246378258611,"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."}}