{"id":"W3046469654","doi":"10.1101/2020.07.31.231621","title":"A Unified Framework for Lineage Tracing and Trajectory Inference","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Royal Commission for the Exhibition of 1851; Natural Sciences and Engineering Research Council of Canada; Burroughs Wellcome Fund","keywords":"Tracing; Inference; Lineage (genetic); Trajectory; Computer science; Leverage (statistics); Theoretical computer science; Artificial intelligence; Biology; Gene; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002478569,0.0004892651,0.0004699692,0.00007603269,0.0001387163,0.0001556921,0.000385484,0.0009155179,0.00000584661],"category_scores_gemma":[0.0005060053,0.0005484999,0.0001831087,0.0001308999,0.000121523,0.000006707444,0.0002276585,0.0006201823,0.000003240349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003392377,"about_ca_system_score_gemma":0.0003996327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001903734,"about_ca_topic_score_gemma":0.00000443574,"domain_scores_codex":[0.9979188,0.00007121294,0.0004043337,0.001042094,0.0001518909,0.0004116335],"domain_scores_gemma":[0.9985234,0.00007518258,0.0002187699,0.0006897826,0.0002229551,0.0002698876],"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":[0.0001321557,0.00007135552,0.002047505,0.0005682817,0.0001363591,0.000007716612,0.0000183893,0.00006646864,0.996268,0.0005993798,0.00007844948,0.000005945076],"study_design_scores_gemma":[0.0008047207,0.0002916397,0.01029132,0.0003806666,0.0001610464,1.863178e-8,0.000005656775,0.0009296628,0.9805499,0.00002558346,0.00555355,0.00100623],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8178129,0.001644403,0.178425,0.0001914376,0.000776757,0.0007067014,0.0003183178,0.0001186651,0.00000579988],"genre_scores_gemma":[0.955423,0.0005218576,0.04231527,0.0004703551,0.0009960248,0.0001437731,0.000002944609,0.0001231612,0.000003588113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1376101,"threshold_uncertainty_score":0.9996967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02571230152011791,"score_gpt":0.2461365681059559,"score_spread":0.220424266585838,"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."}}