{"id":"W2970153313","doi":"10.1101/749267","title":"Variant calling for cpn60 barcode sequence-based microbiome profiling","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Barcode; Biology; Microbiome; Sequence (biology); Computational biology; DNA sequencing; Genetics; Sequence analysis; Evolutionary biology; Profiling (computer programming); Gene; Computer science","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":[0.003824883,0.0008690357,0.0009453411,0.001599206,0.0006763403,0.001551538,0.001549606,0.001475729,0.001820221],"category_scores_gemma":[0.01418412,0.0005571364,0.0008272514,0.001493997,0.000733632,0.0009525674,0.001199855,0.001823011,0.001757559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006537355,"about_ca_system_score_gemma":0.0007751837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001636867,"about_ca_topic_score_gemma":0.002134037,"domain_scores_codex":[0.9958869,0.001073808,0.0002858676,0.001079784,0.001447291,0.0002264116],"domain_scores_gemma":[0.9959972,0.001630355,0.0005376884,0.0008116089,0.0008666862,0.0001563412],"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":[0.0007038323,0.000169145,0.01113148,0.0004829882,0.000211897,0.0002059271,0.0003023955,0.009415232,0.8632298,0.004813595,0.002411725,0.1069221],"study_design_scores_gemma":[0.00003360508,0.0001982266,0.01739361,0.00006946506,0.00007468735,0.0006994104,0.00009406566,0.2126218,0.7527817,0.005586137,0.0102987,0.0001486404],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1132577,0.0005341055,0.8759823,0.0001748723,0.0001815874,0.0002298982,0.002379343,0.005286663,0.001973533],"genre_scores_gemma":[0.168428,0.0001794336,0.8252855,0.0001433117,0.00003383942,0.0002286804,0.003999457,0.0007645183,0.0009373499],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003824883,"threshold_uncertainty_score":0.02022821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02230618554989022,"score_gpt":0.254061346787402,"score_spread":0.2317551612375118,"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."}}