{"id":"W2888458556","doi":"10.1016/j.exphem.2018.06.035","title":"Understanding Cell Fate Decisions in Erythropoiesis Using Quantitative Proteomics and Single-Cell Mass Cytometry","year":2018,"lang":"en","type":"article","venue":"Experimental Hematology","topic":"Erythrocyte Function and Pathophysiology","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ottawa Hospital","funders":"","keywords":"Erythropoiesis; Transcription factor; Mass cytometry; Haematopoiesis; Biology; Cell fate determination; Cell biology; Cell; Transcriptome; Transcription (linguistics); Stem cell; Gene expression; Gene; Genetics; Internal medicine; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0001027624,0.0002002279,0.0004643921,0.0003536606,0.0001641591,0.00001312165,0.00005505247,0.0001968212,0.0002255112],"category_scores_gemma":[0.00005223447,0.0001886128,0.00005713342,0.0003277834,0.0005441844,0.00009127306,0.00008523667,0.0001825901,0.00006585397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003059364,"about_ca_system_score_gemma":0.00005334911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002660621,"about_ca_topic_score_gemma":0.00001233161,"domain_scores_codex":[0.9986973,0.0001039594,0.0003362648,0.0004056756,0.0001161041,0.0003407185],"domain_scores_gemma":[0.9993234,0.0002041431,0.0001146898,0.0001925385,0.00004115778,0.0001241248],"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":[0.0006678286,0.0004168793,0.004383927,0.00003901097,0.0000160388,0.00009438152,0.001610277,0.00000168199,0.9878255,0.004772805,0.0001632042,0.000008494158],"study_design_scores_gemma":[0.002684454,0.001779679,0.0002393482,0.0001043529,0.00002098903,0.0004139241,0.01759693,0.00152291,0.9746847,0.0006580419,0.00008601716,0.0002087],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9649903,0.001591214,0.02843016,0.0001114858,0.0004215295,0.0004131781,0.000004643896,0.00004533038,0.003992132],"genre_scores_gemma":[0.9545567,0.00004095858,0.04488371,0.0003075588,0.0000551446,0.00001499957,0.000009049933,0.00003029954,0.000101593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01645356,"threshold_uncertainty_score":0.7691407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1637685963783939,"score_gpt":0.3553643901879486,"score_spread":0.1915957938095547,"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."}}