{"id":"W2097828850","doi":"10.1093/bioinformatics/btp010","title":"Profiling model T-cell metagenomes with short reads","year":2009,"lang":"en","type":"article","venue":"Bioinformatics","topic":"T-cell and B-cell Immunology","field":"Immunology and Microbiology","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; BC Cancer Agency","funders":"Genome British Columbia; Michael Smith Health Research BC; Genome Canada","keywords":"T-cell receptor; Computational biology; Biology; Profiling (computer programming); Genetics; T cell; Algorithm; Computer science; Immune system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001687788,0.0002585161,0.0003411256,0.0001297101,0.0002010727,0.00002713083,0.0003065391,0.0002754733,0.00007164976],"category_scores_gemma":[0.00001042445,0.0001848226,0.0000829346,0.0001464853,0.0001524058,0.000224416,0.00005294332,0.0003206161,0.0006462329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000313891,"about_ca_system_score_gemma":0.0001011945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003691654,"about_ca_topic_score_gemma":0.000002433707,"domain_scores_codex":[0.9988101,0.0000300993,0.00044073,0.0001702778,0.00005034896,0.0004984887],"domain_scores_gemma":[0.9993196,0.00003257424,0.0001192546,0.0004243862,0.00007426768,0.00002998046],"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":[0.0001189431,0.0001907833,0.0002330019,0.0000396097,0.0001048613,0.000004755379,0.000865982,0.0004021943,0.9871416,0.001379497,0.002232403,0.007286371],"study_design_scores_gemma":[0.001443942,0.000849828,0.00006991375,0.0000257546,0.0001488741,0.0001539864,0.0008616231,0.00411571,0.9793512,0.000262891,0.01217112,0.0005451753],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8261604,0.003909401,0.02562382,0.0004486051,0.0005431341,0.00095191,0.00006198112,0.0006125463,0.1416882],"genre_scores_gemma":[0.9614736,0.0001430588,0.02630841,0.0005439964,0.00001751723,0.00001110294,0.0001903078,0.0000163811,0.01129566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1353132,"threshold_uncertainty_score":0.8306231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01720317905675457,"score_gpt":0.2234832764413907,"score_spread":0.2062800973846361,"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."}}