{"id":"W2810745302","doi":"10.1083/jcb.201711048","title":"A timer for analyzing temporally dynamic changes in transcription during differentiation in vivo","year":2018,"lang":"en","type":"article","venue":"The Journal of Cell Biology","topic":"Immune Cell Function and Interaction","field":"Immunology and Microbiology","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; Directorate for Biological Sciences; University of Toronto; Imperial College London; University College London; University of Tokushima; Biotechnology and Biological Sciences Research Council; Great Ormond Street Hospital Charity","keywords":"Timer; T-cell receptor; Biology; In vivo; Cell biology; Cellular differentiation; FOXP3; Transcription factor; Signal transduction; T cell; Computational biology; Immunology; Gene; Genetics; Computer science","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":[0.0008868458,0.0006967102,0.0005858685,0.00148131,0.0006491854,0.0007459717,0.0009319471,0.0007032811,0.003935355],"category_scores_gemma":[0.000994305,0.0005129261,0.0006653097,0.001225869,0.001012263,0.001255667,0.000751989,0.001632826,0.0008244448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007942765,"about_ca_system_score_gemma":0.0005243956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005989822,"about_ca_topic_score_gemma":0.0009509248,"domain_scores_codex":[0.9995927,0.00006559578,0.00003184614,0.0001664475,0.0001005307,0.00004284933],"domain_scores_gemma":[0.9995791,0.0001480873,0.00008619432,0.00008636819,0.00003937696,0.00006089861],"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":[0.0002020957,0.00003731566,0.00088658,0.0001720772,0.00001684779,0.00009756617,0.0001099993,0.0007402162,0.9650384,0.007627678,0.0005200758,0.02455111],"study_design_scores_gemma":[0.00008016663,0.0004755918,0.004761601,0.00006178948,0.00008511845,0.0007503047,0.0001297779,0.04235091,0.9167498,0.006492377,0.02797031,0.00009217536],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.062976,0.001640452,0.9276659,0.0001282165,0.0003129376,0.0001512713,0.0007851684,0.003307713,0.003032404],"genre_scores_gemma":[0.242063,0.002429495,0.7480729,0.0001964008,0.0000936777,0.001215577,0.0009826936,0.000876367,0.004069859],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003935355,"threshold_uncertainty_score":0.01316512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01072682489833296,"score_gpt":0.2416920545689903,"score_spread":0.2309652296706574,"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."}}