{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004858988,0.000381029,0.0004410016,0.001206824,0.0002185467,0.0006138398,0.00031624,0.0004481483,0.001000268],"category_scores_gemma":[0.0002797964,0.0002037945,0.0002491026,0.0003001515,0.0002523416,0.0002936359,0.0005446284,0.0008735258,0.0005950431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004807131,"about_ca_system_score_gemma":0.000245307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003050542,"about_ca_topic_score_gemma":0.0006222071,"domain_scores_codex":[0.9994696,0.00004652922,0.00005319767,0.0001413361,0.0001981758,0.00009132501],"domain_scores_gemma":[0.9996144,0.00007825079,0.0001141604,0.00005604644,0.0000604629,0.00007669302],"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":[0.00003566274,0.00001336411,0.0003297118,0.00001335933,0.0000025001,0.00001250997,0.00001493313,0.00007944774,0.9972206,0.000161689,0.00003594073,0.00208031],"study_design_scores_gemma":[0.00001153489,0.0001091074,0.003137759,0.000007842938,0.00001293361,0.0001114172,0.00001774449,0.002300561,0.992474,0.0002318346,0.001580593,0.000004647288],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9414142,0.001141076,0.05264233,0.0001473931,0.00003344445,0.0001665992,0.0008164142,0.0007784226,0.002860091],"genre_scores_gemma":[0.9318389,0.0009984892,0.05928889,0.0004184539,0.00003504063,0.0002078972,0.002277745,0.0004437656,0.004490944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001206824,"threshold_uncertainty_score":0.003487825,"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."}}