{"id":"W4366162939","doi":"10.1038/s41592-023-01848-5","title":"BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets","year":2023,"lang":"en","type":"article","venue":"Nature Methods","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":55,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health; University of British Columbia; University of Alberta","funders":"Oak Ridge National Laboratory; National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Eye Institute; Lawrence Berkeley National Laboratory; National Institute of Mental Health; Imperial College London; Southeast University; Engineering and Physical Sciences Research Council; École Polytechnique Fédérale de Lausanne; U.S. Department of Energy; Tencent; National Institutes of Health; University of Cambridge; Agence Nationale de la Recherche; Wellcome Trust; Beijing University of Technology; Howard Hughes Medical Institute","keywords":"Computer science; Tracing; Benchmarking; Benchmark (surveying); Data mining; Visualization; Set (abstract data type); Cluster analysis; Artificial intelligence; Data set; Random forest; Ground truth; Machine learning; Pattern recognition (psychology)","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":[0.0009044033,0.0001129643,0.0002274747,0.000205729,0.00002429511,0.000006936582,0.0001834713,0.0002086087,0.000001271572],"category_scores_gemma":[0.00044283,0.0001071074,0.00005481731,0.0004595461,0.00003700433,0.000004583093,0.0001510842,0.0001464834,1.102678e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004912248,"about_ca_system_score_gemma":0.00002179845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005912976,"about_ca_topic_score_gemma":0.000007148156,"domain_scores_codex":[0.9990246,0.0001495005,0.0002561635,0.0003217761,0.00008250236,0.0001654845],"domain_scores_gemma":[0.9993589,0.00008159123,0.00009076751,0.0003763854,0.00005085153,0.00004153913],"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":[0.00007856645,0.00002200179,0.0005621046,0.0001018922,0.00001890141,0.000001752336,0.00004627663,0.00003971637,0.9794711,9.278688e-7,0.009442394,0.01021434],"study_design_scores_gemma":[0.000204861,0.0003301437,0.003571972,0.00003867085,0.00002861155,0.000004709712,0.00001462866,0.00807606,0.9609267,0.000004167328,0.02671041,0.00008903001],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925122,0.0008001712,0.005726108,0.0001249619,0.00003503761,0.0003977439,0.000199562,0.00005973591,0.0001444991],"genre_scores_gemma":[0.5602764,0.0002538929,0.4380988,0.0002431029,0.00006173543,0.00005656642,0.0008205341,0.00003700848,0.0001519614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4323727,"threshold_uncertainty_score":0.4367712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01044471671180667,"score_gpt":0.37768102472112,"score_spread":0.3672363080093133,"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."}}