{"id":"W1497374306","doi":"10.1038/ni1110-975","title":"A model for harmonizing flow cytometry in clinical trials","year":2010,"lang":"en","type":"article","venue":"Nature Immunology","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":151,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute of Infection and Immunity","funders":"National Institutes of Health","keywords":"Flow cytometry; Sample (material); Flow (mathematics); Clinical trial; Computer science; Computational biology; Medicine; Medical physics; Immunology; Pathology; Biology; Chemistry; Mathematics; Chromatography","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001936351,0.0001537667,0.0004123976,0.00009719554,0.00004954675,0.00001596822,0.0002842816,0.001703326,0.00001002747],"category_scores_gemma":[0.002678298,0.0001382141,0.0002717197,0.00009161138,0.0001085402,0.000003370678,0.0000529507,0.001134845,0.000003583355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007770188,"about_ca_system_score_gemma":0.0001186552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005901054,"about_ca_topic_score_gemma":0.000176253,"domain_scores_codex":[0.9985027,0.0001687966,0.0005894264,0.0003877698,0.00005672971,0.0002945615],"domain_scores_gemma":[0.9992059,0.0001921396,0.000124464,0.0003427948,0.00009118475,0.00004345823],"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":[0.0006844805,0.0001291846,0.002486596,0.000008213323,0.00007497244,0.000001760414,0.00002556203,0.00004242831,0.9756075,0.0002564046,0.000652957,0.02002995],"study_design_scores_gemma":[0.01788394,0.001458682,0.01472036,0.00004402299,0.0001583863,0.00008084466,0.00007660403,0.1352681,0.7063612,0.00372982,0.1189262,0.001291878],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9728224,0.002377178,0.02134844,0.0004248554,0.002402873,0.0003656727,0.00004841084,0.0000187375,0.0001914263],"genre_scores_gemma":[0.9828447,0.0002306009,0.01518882,0.000755795,0.0005321694,0.00003918859,0.0001246902,0.0000276222,0.0002564073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2692463,"threshold_uncertainty_score":0.9995927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1023589057842454,"score_gpt":0.3884912442408593,"score_spread":0.2861323384566139,"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."}}