{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001084301,0.0006525749,0.0006070877,0.0004544571,0.0004277735,0.0008551796,0.0007307345,0.0009620709,0.001022824],"category_scores_gemma":[0.003239438,0.0004531907,0.000837317,0.0006883361,0.0004927818,0.0009681355,0.0005346653,0.0007778213,0.0005415487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008724694,"about_ca_system_score_gemma":0.0005984611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002757478,"about_ca_topic_score_gemma":0.002406971,"domain_scores_codex":[0.9993869,0.0001768431,0.00003606868,0.000212547,0.0001314718,0.00005601813],"domain_scores_gemma":[0.9983073,0.0009091432,0.0001889557,0.0002161004,0.0002833117,0.00009507807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000581397,0.0001443626,0.02299063,0.0002477369,0.000107166,0.0002048795,0.0002163961,0.8624662,0.09784611,0.002302552,0.0004064175,0.01248617],"study_design_scores_gemma":[0.00002503863,0.0001864047,0.003572737,0.00001071398,0.00002311824,0.00007387747,0.00007171112,0.966361,0.02568859,0.003021531,0.0009438343,0.00002137342],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7615795,0.000314701,0.2324053,0.0001861054,0.00004964334,0.000152697,0.002806674,0.001191156,0.001314266],"genre_scores_gemma":[0.837769,0.0002823013,0.1544007,0.0001891479,0.00002321967,0.0003760932,0.00565339,0.0002310138,0.001075058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002757478,"threshold_uncertainty_score":0.006330192,"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."}}