{"id":"W2157443892","doi":"10.1016/j.exphem.2015.05.006","title":"Index sorting resolves heterogeneous murine hematopoietic stem cell populations","year":2015,"lang":"en","type":"article","venue":"Experimental Hematology","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Cambridge Institute for Medical Research, University of Cambridge; Biotechnology and Biological Sciences Research Council; Medical Research Council; Blood Cancer UK; Leukaemia and Lymphoma Research; Canadian Institutes of Health Research; National Institute for Health and Care Research; Cancer Research UK; NIHR Cambridge Biomedical Research Centre; Wellcome Trust","keywords":"Stem cell; Flow cytometry; Cell sorting; Biology; Haematopoiesis; Cell; Cell biology; Stem cell marker; Hematopoietic stem cell; Cluster of differentiation; Single-cell analysis; Cell division; Computational biology; Molecular biology; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008308072,0.0002181885,0.0002539272,0.00005096862,0.0001062385,0.00002261202,0.0001975191,0.0001979674,0.000031355],"category_scores_gemma":[0.000007222246,0.0002221549,0.000108882,0.00006759141,0.0001040204,0.000005900712,0.0001264797,0.00009388303,0.00003958594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003052822,"about_ca_system_score_gemma":0.00005191689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004739974,"about_ca_topic_score_gemma":0.00003247151,"domain_scores_codex":[0.9986842,0.00007024836,0.0003619276,0.0003987978,0.0001385379,0.0003463387],"domain_scores_gemma":[0.9993104,0.000007219672,0.0001074257,0.0003429369,0.00004487671,0.0001871868],"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":[0.0004507875,0.001221317,0.0470918,0.00008172389,0.00007857779,0.0001226736,0.001973099,0.0007047217,0.9390446,0.005751543,0.003109045,0.000370121],"study_design_scores_gemma":[0.0009818245,0.0003288558,0.00005890552,0.00001001024,0.00000839397,0.0003616581,0.0008608961,0.0002979929,0.9948108,0.0001375041,0.001884626,0.0002585344],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858053,0.009669268,0.00145787,0.00006015583,0.0005245244,0.0001905367,0.000005498609,0.00004851278,0.002238355],"genre_scores_gemma":[0.9981062,0.000008313385,0.0008473753,0.0002099572,0.0001342899,0.0000380686,0.00007753303,0.00004296091,0.0005352387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05576621,"threshold_uncertainty_score":0.9059215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.043575322046652,"score_gpt":0.2863768172034147,"score_spread":0.2428014951567627,"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."}}