{"id":"W4327714827","doi":"10.1101/2023.03.15.532767","title":"Limitations and Optimizations of Cellular Lineages Tracking","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Barcode; Inference; Dropout (neural networks); Computer science; DNA barcoding; Biology; Tracking (education); Artificial intelligence; Machine learning; Evolutionary biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01413133,0.00160043,0.001778023,0.0009294584,0.001002809,0.003089509,0.003299812,0.001928101,0.003409454],"category_scores_gemma":[0.05774428,0.00118486,0.001246263,0.001437851,0.001939704,0.003560949,0.003112806,0.003368954,0.002776545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001881502,"about_ca_system_score_gemma":0.002060448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003507241,"about_ca_topic_score_gemma":0.003357467,"domain_scores_codex":[0.9896568,0.004734452,0.0007178086,0.002196743,0.00223776,0.0004564863],"domain_scores_gemma":[0.9413672,0.04119607,0.001367106,0.011185,0.003995617,0.0008890616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001641672,0.0003265302,0.008899263,0.003668646,0.0003729025,0.0004141589,0.0009118271,0.2140896,0.39873,0.07033417,0.01212894,0.2884822],"study_design_scores_gemma":[0.000100727,0.0004997626,0.007105559,0.000559089,0.0002408927,0.001024529,0.0005213462,0.5114179,0.2819242,0.1303964,0.06596512,0.0002445908],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03633071,0.003223619,0.9451204,0.003428778,0.0002858513,0.0002427282,0.001391713,0.003781907,0.006194356],"genre_scores_gemma":[0.2824915,0.002714244,0.7062109,0.001313559,0.0001493342,0.001051382,0.001849411,0.001331986,0.002887565],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01413133,"threshold_uncertainty_score":0.07473457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04450333731489614,"score_gpt":0.2267416090203647,"score_spread":0.1822382717054686,"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."}}