{"id":"W4214917972","doi":"10.21203/rs.3.rs-1401703/v1","title":"Novel approach to analysis of the immune system using an ungated model of immune surface marker abundance to predict health outcomes","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"National Medical Research Council; Medical Research Council; Fonds de Recherche du Québec - Santé; Biomedical Research Council; Canadian Institutes of Health Research; Université de Sherbrooke","keywords":"Immune system; Gating; Abundance (ecology); Raw data; Computer science; Computational biology; Data science; Biology; Immunology; Mathematics; Statistics; Ecology; Neuroscience","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":[0.001133077,0.0007074134,0.0006535978,0.0006356294,0.0002470182,0.0009889784,0.001133379,0.0009255775,0.001385533],"category_scores_gemma":[0.002235853,0.0002927958,0.0007967277,0.0004337909,0.0007083211,0.0006151004,0.0007474099,0.00106404,0.0002113213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009017775,"about_ca_system_score_gemma":0.0006448544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007359006,"about_ca_topic_score_gemma":0.004688763,"domain_scores_codex":[0.999738,0.00009641949,0.000009086129,0.0000941969,0.00002569545,0.0000365261],"domain_scores_gemma":[0.9991578,0.0005362597,0.0001014095,0.0000626269,0.00008777866,0.00005416165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001018486,0.00008781109,0.007030585,0.00002658579,0.00009409038,0.00008662666,0.0000355553,0.9747653,0.003214361,0.002792399,0.0002793198,0.01148545],"study_design_scores_gemma":[0.000002128094,0.000008525919,0.0003962652,0.000001205558,0.000003948581,0.000003990358,0.000001800974,0.9981838,0.0001503785,0.001207,0.00003890377,0.000002002275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.38517,0.0006724421,0.6102012,0.0009881333,0.0001085768,0.00005912574,0.0004909282,0.0005221042,0.001787439],"genre_scores_gemma":[0.9695444,0.0001687151,0.02721001,0.0001617956,0.00005966983,0.00008170939,0.0002270075,0.00003113611,0.002515686],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007359006,"threshold_uncertainty_score":0.01463234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1000110654995537,"score_gpt":0.3683213299249578,"score_spread":0.2683102644254041,"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."}}